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If your business involves online transactions or stores sensitive customer information, fraud risk is always a challenge that requires continuous attention. A single weakness in protection can lead to financial losses, disputed order cancellations, and eroded customer trust. More importantly, post-incident investigations are often time-consuming and fail to fully undo the damage.

Therefore, today's anti-fraud platforms no longer only pursue "accuracy" but also highly value "speed." They monitor activity in real time, dynamically adapt to new attack methods, and provide actionable insights as events unfold. At the heart of these systems lies reliable, high-quality data—a crucial element of which is real-time network and IP intelligence.

The following analysis will examine how modern anti-fraud solutions can identify threats faster and what essential capabilities should be considered when evaluating relevant platforms.

Choosing an Anti-Fraud Management System That Supports Fast, Accurate Detection

Not all anti-fraud tools are designed for real-time decision-making. Some still rely on models that use batch updates, static rules, or delayed scoring, typically updating months after IP data changes.

Faster detection capabilities typically stem from the organic combination of the following capabilities: Identity and Network Data Enrichment

When email domains, phone numbers, IP addresses, and device information are correlated and analyzed, many patterns naturally emerge. Network-level signals often reveal issues that user-level data alone cannot reflect. For example, a large number of accounts registering within a short period using only slightly modified email addresses from the same IP address—such anomalies are difficult to hide if signal enrichment is performed in real time.

Timely Updated Device and Browser Fingerprints

Behavior can change mid-session. A trusted device suddenly switching to an uncommon browser or emulator can trigger risk alerts.

Frequency and Behavioral Pattern Checks

Rapid, consecutive login failures, repeated attempts, or unusual browsing paths are often early signs of attacks. Real-time systems can intervene before accounts are compromised.

Scalable Machine Learning and System Integration

During major sales events, new product launches, or seasonal traffic peaks, decision-making speed should not be slow, nor should blind spots exist. This requires external data sources to have the same elastic scaling capabilities.

When these elements work in tandem, anti-fraud tools can shift from reactive response to proactive protection, maintaining revenue and trust while reducing user friction.

How Modern Anti-Fraud Solutions Continuously Address Emerging Threats

The most significant change in fraud protection lies in timing. Today's anti-fraud systems no longer wait for aggregated reports; they monitor as activity occurs.

Every login attempt, every transaction, every device interaction enters a risk assessment in real time. Suspicious behavior can be flagged before an attack fully develops. Teams no longer just do post-incident reviews; they respond while there's still an opportunity to intercept.

1. Instant Signal Access

The system needs to capture all critical events without delay: logins, transactions, account changes, device data, session behavior, and network signals that help identify automated traffic, spoofed locations, or reused infrastructure. Most fraudulent activities scale up gradually through repeated attempts; real-time access ensures that every interaction—including continuous queries to IP addresses—is recorded promptly. Without up-to-date IP location information, early anomalies can easily be overlooked.

2. Continuous Event Stream Processing

Capturing signals is only the first step. Stream processing allows this information to flow continuously and undergo uninterrupted analysis. Each event is compared against historical patterns and related accounts to instantly identify abnormal sequences or recurring behaviors. Even during peak traffic periods, the system continues to operate, providing teams with early warnings as events unfold, rather than only after the fact. For example, when a series of suspicious logins are observed, real-time analysis can signal within seconds, allowing time for proactive intervention.

3. Adaptive Machine Learning Based on Real-Time Behavior

Static rules struggle to handle constantly evolving tactics, while machine learning models can. As events flow in, the model continuously compares current behavior with known fraud patterns and the latest trends. Abnormal access paths, sudden IP changes, or abnormal transactions are identified without waiting for manual updates. Over time, the system gradually understands your platform's "normal" baseline, reducing false positives and focusing attention on genuine risks.

4. Millisecond-Level Scoring and Automated Decision-Making

Every action receives an immediate risk assessment. The system integrates transaction information, behavioral characteristics, device fingerprints, network signals (including IP lookup results), and model output to assign a risk score. High-risk actions are automatically blocked, medium-risk actions enter an additional verification process, and normal behavior is smoothly allowed. The entire decision-making process is completed within milliseconds, preventing fraudulent activity while avoiding significant delays for legitimate users.

5. Early Anomaly Detection

Many attacks initially release weak but identifiable signals: a sudden increase in consumption frequency, previously unseen geographical locations, recurring or constantly rotating IPs, and clearly automated behavioral patterns. By identifying these early changes, teams can intervene while the problem is still manageable, rather than waiting for the damage to escalate. Continuous, lightweight queries of IP addresses (e.g., verifying IP activity and response patterns using tools like iping) help quickly distinguish between genuine user traffic and bot traffic, providing an additional dimension of reference for early anomaly detection.

Why IP Intelligence is Crucial for Real-Time Anti-Fraud

Even the best anti-fraud systems cannot operate in a vacuum. They require external data input to determine the true source of traffic and whether the behavior is reasonable at a macro level. IP data is often one of the early signals of anomalies, especially in bot traffic, credential stuffing attacks, or coordinated attacks that attempt to hide themselves by rotating devices and mimicking normal user behavior. Continuous and frequent IP lookups help the system identify whether traffic originates from known data centers, proxies, or high-risk areas, thereby improving the accuracy of risk assessment.

Real-time visibility is currently the most effective line of defense.

When your anti-fraud management system can promptly receive signals, dynamically analyze behavioral patterns, and take action before suspicious transactions are completed, your team gains a real defensive advantage. This means fewer disputed cancellations and less manual review work. More importantly, it ensures a smoother customer experience and an anti-fraud strategy that won't easily become ineffective due to changes in attack methods.

Implementing precision advertising based on IP geolocation is a marketing strategy that leverages user IP address information to digitize and refine advertising efforts, enabling the display of personalized content based on a visitor's geographical location. Compared to the "wide net" approach of traditional advertising, IP-based advertising acts more like a precise "sniper rifle"—allowing you to reach genuinely interested potential customers at exactly the right location.

What Is IP Geolocation?

At the core of IP geolocation lies the process of identifying the IP address of every user visiting your website or application, and subsequently determining their geographical location. Based on this information, you can:

1. Push promotional offers for nearby physical stores.

2. Display localized languages ​​and currencies based on the user's region.

3. Provide differentiated product recommendations tailored to users in different geographical areas.

4. Identify returning visitors and offer them loyalty rewards.

Through frequent IP lookups and precise IP positioning, you can concentrate your marketing budget on high-conversion regions, avoiding wasteful spending on non-target audiences.

Two Common Forms of IP Positioning That Boost Precision Advertising Operations

1. Geo-targeting

Identifies a user's city or regional network based on their IP address. This method is ideal for marketing campaigns differentiated by city or country—for instance, automatically switching website content to German when a user's IP address is detected as originating from Germany.

2. Geofencing

Involves setting up a virtual "fence" around a specific physical location; when a user's device enters or exits this designated area, an ad push is automatically triggered. While this method typically relies on GPS to obtain precise device-level location data, GPS signals are not always available. In such instances, IP positioning serves as an effective supplementary signal—by quickly performing an IP lookup to determine the approximate region of the user's network, it aids in the geofencing trigger decision-making process and expands the range of applicable scenarios. Consequently, this approach places high demands on both the accuracy and response speed of the IP positioning system.

Regardless of which method you choose, a stable and reliable IP lookup service serves as the fundamental prerequisite. Behind every single ad placement lies the necessity for rapid and accurate IP positioning of the user, enabling the system to determine exactly what content should be displayed.

How to Implement IP Geolocation and Enhance Precision Advertising Operations?

Step 1: Define Your Target Scenarios

Content Localization: Automatically translate website content and switch currency units based on the visitor's IP address.

Drive In-Store Traffic: Set up geofences around commercial districts or competitor locations to push promotional coupons to potential customers passing through the area. Target Specific Audiences: If you are selling products geared toward university students, you can increase ad exposure within IP ranges associated with areas near higher education institutions.

Enhance Customer Experience: Identify returning visitors via their IP addresses to display personalized welcome messages or exclusive discounts.

Step 2: Select a Reliable Source for IP Geolocation Data

The quality of your IP geolocation data directly impacts the accuracy of your advertising decisions. You need an IP lookup tool capable of providing the following capabilities:

High Accuracy: City-level accuracy, postal code-level precision, and confidence radius data.

Data Freshness: Rapid updates following changes to IP blocks, avoiding the use of outdated WHOIS records.

Network Type Identification: Distinguishing between residential IPs, commercial IPs, mobile gateways, data centers, and VPNs/proxies.

Privacy Detection: Filtering out fraudulent traffic originating from proxies or residential proxy pools to avoid paying for bot traffic.

Step 3: Design Trigger Rules and Delivery Strategies

Once you have determined which IP ranges or geographic locations will trigger your ads, set frequency caps to prevent the same IP address from being subjected to excessive ad exposure. Finally, refine your ad delivery strategy by incorporating additional conditions, such as time of day and device type.

Step 4: Monitor Performance and Iterate

Leverage the feedback data provided by your advertising platforms to analyze the Click-Through Rate (CTR), store visit rate, or conversion rate generated by different IP regions. If you discover that conversion rates in a high-traffic city are unusually low, it may indicate a discrepancy in the IP geolocation data for that region, or that a significant portion of the traffic is originating from proxies. In such cases, you can use an IP lookup tool to verify the network type of the traffic in question.

IPing Provides You with Reliable Information

Every click, login, purchase, stream, and API request begins with the same thing: an IP address.

For most users, an IP address is an invisible piece of underlying infrastructure. But for developers, cybersecurity teams, fraud analysts, and growth platforms, IP intelligence has become one of the most critical real-time signals on the internet.

In the modern sense, "IP" is no longer just about mapping an IP address to a specific city. It has evolved into a comprehensive intelligence layer that helps companies understand:

Where traffic actually originates

Who operates the network in question

Whether a specific connection is trustworthy

How users should be routed, secured, or personalized

Companies building the next generation of internet products are increasingly relying on IP data to make instantaneous decisions at a global scale.

Why IP Intelligence Matters

An IP address can reveal far more than just geographic location.

Depending on the provider and dataset, a system integrating with an IP intelligence platform can access information regarding:

Geographic region

ASN and ISP ownership

Hosting service providers

VPN or proxy usage

Mobile carrier information

Residential proxy signals

Corporate affiliation

Abuse contact channels

Network reputation

These signals help organizations answer critical questions in milliseconds:

Is this a suspicious login attempt? Is the traffic originating from a data center? Is the user accessing local content?

Modern fraud detection systems are increasingly combining geolocation data with ASN and anonymity detection to improve user experience while simultaneously reducing false positives.

Core Capabilities of Modern IP Platforms

1. IP Geolocation

The foundation of IP intelligence is geolocation: country, city, region/state, postal code, time zone, and latitude/longitude.

This capability underpins: localization, regulatory compliance, analytics, regional pricing, and content distribution.

However, the accuracy of geolocation information is not uniform across all locations. Research indicates that mobile networks and certain other sources may exhibit geolocation errors significantly larger than those found in fixed broadband networks.

This means that modern platforms are increasingly focusing on confidence scores and contextual signals, rather than pretending that IP geolocation is perfectly precise.

2. ASN and Network Intelligence

An ASN (Autonomous System Number) identifies the organization that operates a specific network. This is crucial because traffic originating from the following sources exhibits vastly different behaviors:

Residential ISPs

Cloud Service Providers

Hosting Companies

Mobile Carriers

Different ASNs (Autonomous Systems) carry distinct traffic characteristics: AWS traffic may signal automation or web crawlers; university networks may generate high volumes of shared user activity; and mobile carriers are often associated with frequent IP address rotation. Since these differences directly impact the assessment of fraud risk, user profiling, and behavioral expectations, ASN intelligence—the ability to identify the type of network to which an IP address belongs—has become an indispensable foundational layer in security and fraud modeling.

3. VPN and Proxy Detection

Privacy infrastructure has experienced explosive growth. Today's platforms must be capable of detecting:

VPNs

Tor Exit Nodes

Open Proxies

Relay Services

Residential Proxy Networks

This is particularly critical for sectors such as FinTech, e-commerce ticketing platforms, the gaming industry, and ad verification.

Residential proxies are especially difficult to detect because they utilize legitimate, ISP-assigned IP addresses rather than readily identifiable data center infrastructure.

4. Carrier and Mobile Intelligence

Mobile traffic behaves differently than desktop traffic:

NAT Gateways

IP Rotation

Carrier Routing

Shared Infrastructure

Carrier intelligence helps to: optimize the mobile experience, analyze application traffic, enhance fraud detection, and identify telecommunications routing issues. The continued global growth of mobile internet usage is making each of these tasks increasingly complex—which is precisely why carrier intelligence is becoming ever more vital.

5. Enterprise and Corporate Attribution

Certain IP ranges can be mapped to specific organizations. This enables: B2B lead generation, account-based marketing (ABM), visitor identification, and sales intelligence.

For SaaS companies, IP intelligence can transform raw traffic data into actionable business insights.

The Shift from Geolocation to Decision Intelligence

The most significant industry shift is that this is no longer viewed merely as a passive lookup service; it is evolving into a real-time decision-making engine.

Platforms are no longer simply asking:

“Where is this IP address located?”

Instead, they are beginning to ask:

Is this traffic risky?

Is this user trustworthy?

Should we challenge this session?

Is this behavior anomalous?

Introduction: An IP Address is Just a Number, ASN is the Identity

In the risk control domain, many people only focus on "what address is this IP," ignoring the critical question: "which network does this IP belong to?"

In 2026's anti-scraping and anti-fraud battleground, a key insight is reshaping the industry: an IP address is just a number, while the ASN is its true "identity card" in the network world. The same IP address might belong to a residential broadband user or to a batch of IPs from a cloud provider—the trust weight difference in risk control systems is enormous.

This article explores how ASN (Autonomous System Number) becomes the golden partner for precise street-level IP geolocation when considering how to query IP addresses, and how IPing integrates ASN data into IP data API call examples to help enterprises build more accurate risk control systems.

 Part 1: What is ASN? Why It's More "Real" Than IP

ASN stands for Autonomous System Number. Every network connected to the internet—whether residential broadband, cloud services, or enterprise leased lines—is assigned a unique ASN, similar to a "ID number" in the network world.

The core value of ASN lies in representing "network operator identity":

- AWS (Amazon Cloud) owns its own ASN, DigitalOcean has another, and cheap VPS used by hackers also have specific ASNs

- Risk control systems bucket traffic by ASN: if a request comes from a known cloud service provider's ASN, that IP's trust weight is far lower than residential broadband

- More critically: if a large number of high-frequency probing requests emerge within the same ASN block, the risk control system can determine that this network segment has automated attacks, downgrading or throttling the entire block

 Part 2: ASN + IP Type: The Golden Combo for Identifying "Dirty IPs"

 2.1 Correspondence Between IP Type and ASN

Different IP types typically correspond to different ASNs:

| IP Type | ASN Characteristics | Risk Level |

|--------|---------------------|------------|

| Residential IP | Local ISP's ASN, belongs to home users | Low Risk |

| Mobile IP | Carrier mobile network ASN | Medium-Low Risk |

| IDC IP (Data Center) | Cloud service provider or IDC ASN (e.g., AWS 16509) | Medium-High Risk |

| Proxy/VPN Exit | VPN service provider's ASN | High Risk |

 2.2 Real Case: The "Fake Residential IP" in Frankfurt

A European e-commerce platform discovered a batch of registered accounts from Frankfurt, Germany. Querying the IP address showed geolocation as Frankfurt—everything appeared completely normal on the surface.

But after integrating IPing's ASN data, the problem surfaced: all these IPs came from the same ASN block—a provider offering "residential IP proxy" services. Although querying the IP location showed Frankfurt, the ASN belonged to a commercial proxy service provider's IP block, not a regular home broadband ISP.

This is the penetrating power of ASN: looking at the IP address alone shows it as normal, but checking the ASN reveals the truth.

 Part 3: Practice—Python Integration of IPing + ASN Detection

```python

import requests

def check_ip_asn_reputation(ip: str) -> dict:

    """

    Combine IPing API to query IP geolocation and ASN data, evaluate IP reputation

    """

     Call IPing API to query IP basic data

    iping_response = requests.get(

        f"https://api.iping.cn/v1/query?ip={ip}&key=YOUR_IPING_KEY"

    )

    iping_data = iping_response.json()

     Extract ASN information (IPing returns data containing ISP and ASN fields)

    asn = iping_data.get("asn")

    isp = iping_data.get("isp")

    ip_type = iping_data.get("ip_type")

    location = iping_data.get("country") + "/" + iping_data.get("city")

     High-risk ASN list (can be dynamically updated with threat intelligence)

    HIGH_RISK_ASNS = {

        "AS1234",   Known VPN provider

        "AS5678",   Cheap VPS supplier

    }

     Core judgment logic

    result = {

        "ip": ip,

        "location": location,

        "asn": asn,

        "isp": isp,

        "ip_type": ip_type,

        "risk_level": "low"

    }

     Check if it's a known high-risk ASN

    if asn in HIGH_RISK_ASNS:

        result["risk_level"] = "high"

        result["reason"] = f"ASN {asn} belongs to a known high-risk network segment"

     Check if it's a cloud service provider ASN (low trust weight)

    elif isp and any(provider in isp.lower() for provider in ["aws", "digitalocean", "vultr", "linode"]):

        result["risk_level"] = "medium"

        result["reason"] = f"ISP {isp} belongs to a cloud service provider ASN with higher batch behavior risk"

     Check if it's a data center ASN

    elif ip_type == "IDC":

        result["risk_level"] = "medium"

        result["reason"] = f"IP type is data center, ASN {asn} may have batch registration behavior"

    return result

 Usage example

test_ip = "35.156.123.45"   An IP in Frankfurt, Germany

result = check_ip_asn_reputation(test_ip)

print(result)

 Example output:

 {'ip': '35.156.123.45', 'location': 'Germany/Frankfurt',

  'asn': 'AS16509', 'isp': 'Amazon AWS',

  'ip_type': 'IDC', 'risk_level': 'medium',

  'reason': 'ISP Amazon AWS belongs to cloud service provider ASN with higher batch behavior risk'}

```

 Part 4: Building an ASN-Aware Risk Control Architecture

 4.1 Three-Layer Risk Control Architecture

```

Access Layer → Extract client IP

    ↓

Enrichment Layer → Call IPing API to get:

    ① Query IP location (street-level precise positioning)

    ② ASN number and ISP name

    ③ IP type (residential/mobile/IDC/proxy)

    ④ Whether it's a proxy (is this IP a proxy)

    ↓

Decision Layer → Comprehensive judgment combining ASN + IP type + behavior patterns

```

 4.2 ASN Reputation Dynamic Maintenance

The ASN reputation database needs dynamic updates. The following scenarios should promptly add ASN to the high-risk list:

- Large number of registration/login requests within the same ASN block in a short time

- Batch scraping behavior associated with ASN-related IPs

- Specific ASNs showing clear automated tool characteristics

```python

def update_asn_blacklist(asn: str, reason: str):

    """Dynamically update ASN blacklist database"""

    with open("asn_blacklist.txt", "a", encoding="utf-8") as f:

        f.write(f"{asn}|{reason}\n")

 Monitoring logic: over 100 registration requests within 5 minutes in the same ASN block

def monitor_asn_registration(asn: str, count: int):

    if count > 100:

        update_asn_blacklist(asn, f"{count} registration requests within 5 minutes, suspected automated batch behavior")

```

 Conclusion

With street-level IP geolocation becoming increasingly precise today, relying on IP addresses alone can no longer effectively identify impersonators. ASN, as the "network identity" behind an IP, is an indispensable second key in the risk control system.

In the field of cybersecurity, proxy IPs, VPNs, and the Tor network are often used by malicious actors to hide their real identities and carry out activities such as click fraud, web scraping, and automated account registration. How can you quickly identify these malicious IPs and protect your business? IPing's proxy detection feature provides an accurate solution.

Why Do You Need to Detect Proxy IPs?

Many malicious activities rely on proxy technologies to conceal real IP addresses:

Click fraud & fake traffic: Black market operations use large numbers of proxy IPs to simulate real users and inflate sales or traffic metrics

Fake account registration: Attackers use proxy IPs to mass-register accounts and carry out credential stuffing attacks

Web scraping: Scrapers use proxy IPs to bypass anti-scraping mechanisms

Fraudulent transactions: Fraudsters hide their true geographic location using proxy IPs to commit cross-border fraud

IPing Proxy Detection Feature

IPing offers a professional proxy detection feature to help you quickly determine whether an IP is a proxy, VPN, or Tor exit node.

Core Capabilities

Proxy type identification: Accurately identify HTTP proxies, SOCKS proxies, VPNs, Tor, and other types

Anonymity level detection: Determine the proxy's anonymity level (transparent, anonymous, elite/high-anonymity)

Threat intelligence correlation: Cross-reference with global threat intelligence databases to flag known malicious proxy IPs

Geolocation lookup: Even when a proxy is used, pinpoint the true geographic location of the real IP (street-level IP geolocation)

How to Check if an IP is a Proxy Using IPing?

Using IPing to query an IP address is very straightforward. Simply visit the IPing website, enter the IP address you want to check, and you will receive detailed proxy detection results.

Python Code Example: Batch Detection of Proxy IPs

Below is a Python example that uses the IPing API to detect proxy IPs in batches:

python

import requestsimport jsondef batch_check_proxy_ips(ip_list):    """    Batch check if IPs are proxies    """    results = []        for ip in ip_list:        # Call IPing API        url = f"https://api.iping.com/v1/proxy/detect?ip={ip}"        headers = {            "Authorization": "Bearer YOUR_API_KEY",            "Content-Type": "application/json"        }                try:            response = requests.get(url, headers=headers, timeout=5)            if response.status_code == 200:                data = response.json()                                result = {                    "ip": ip,                    "is_proxy": data.get("is_proxy", False),                    "proxy_type": data.get("proxy_type", "none"),                    "anonymous_level": data.get("anonymous_level", "unknown"),                    "country": data.get("country", ""),                    "city": data.get("city", "")                }                results.append(result)                                # Print results                print(f"IP: {ip}")                print(f"  Is Proxy: {result['is_proxy']}")                print(f"  Proxy Type: {result['proxy_type']}")                print(f"  Anonymity Level: {result['anonymous_level']}")                print(f"  Geolocation: {result['country']} {result['city']}")                print("-" * 50)                except Exception as e:            print(f"Failed to query IP {ip}: {e}")        return results# Example IP data API calltest_ips = [    "185.220.101.6",    # Known Tor exit node    "89.187.191.12",    # Known VPN server    "8.8.8.8"           # Regular Google DNS]results = batch_check_proxy_ips(test_ips)

Real-World Use Cases

Use Case 1: Preventing Click Fraud

An e-commerce platform noticed that over 500 orders originated from the same proxy IP pool when using its IP location lookup feature. By leveraging IPing's proxy detection capabilities, the platform quickly identified these fraudulent orders and avoided millions of dollars in losses.

Use Case 2: Blocking Fake Account Registration

A social media platform uses the IPing API to check IP addresses in real time during new user registration. When a proxy IP is detected, the system automatically triggers secondary verification (SMS code or facial recognition), effectively stopping mass registration bot attacks.

Use Case 3: Preventing Data Scraping

A content platform used IPing's proxy detection feature to identify that more than 30% of incoming requests came from proxy IPs. The platform implemented rate limiting on these IPs, protecting its content from being scraped maliciously.

The Value of Street-Level IP Geolocation

Beyond proxy detection, IPing also provides precise street-level IP geolocation. Even if a malicious user employs a proxy, IPing uses multiple techniques to pinpoint their true geographic location as accurately as possible.

For example, a financial platform used IPing's street-level geolocation to discover that the actual IP for a transaction was in New York, USA, while the user claimed to be in London, UK. This geographic inconsistency helped the platform flag and block the fraudulent transaction in time.

Conclusion

Proxy IP detection is a critical component of cybersecurity. IPing delivers professional, accurate proxy detection capabilities to help you quickly identify malicious IPs and protect your business. Whether you need to prevent click fraud, block fake account registrations, or stop data scraping, IPing is the optimal choice for you.

Have you ever encountered these situations:

- Account registration immediately blocked

- Frequent CAPTCHA prompts when visiting websites

- Payment blocked by risk control system

- Game account suddenly restricted

The problem might not be your behavior, but that your **IP is "dirty"**.

According to the "2026 Network Security Threat Report":

- 78% of black/gray market attacks use proxy IPs to hide real identity

- 63% of enterprise risk control systems have proxy identification accuracy below 85%

- Average loss from single proxy IP missed detection: 500,000-2,000,000 yuan

Your IP might have entered a "blacklist" due to past abuse. Today, we'll use a **3-step self-check method** to help you uncover your IP's "dark history" and teach you how to use **IPing** tools to maintain IP purity.

---

## Step 1: Check "Criminal Record" —— Is the IP "Bad"?

Just like checking a person's credit record, the first step is to check the IP's "criminal record".

**Why is this important?**

An IP address is like your network ID. If an IP was once used to:

- Send spam emails

- Launch DDoS attacks

- Conduct fraudulent transactions

- Scrape website data

Then this IP will be marked as a "malicious IP" by security databases. When you use this IP to access websites, it will trigger risk control systems.

**How to query IP reputation using IPing?**

Using IPing's API, you can quickly query the reputation score of any IP:

```python

import requests

def check_ip_reputation(ip_address):

    """

    Query IP reputation score

    """

    api_url = "https://api.iping.com/v1/ip/reputation"

    params = {

        "ip": ip_address,

        "api_key": "YOUR_API_KEY"

    }

    response = requests.get(api_url, params=params)

    data = response.json()

    # Parse reputation score

    fraud_score = data.get("fraud_score", 0)

    is_malicious = data.get("is_malicious", False)

    blacklist_sources = data.get("blacklist_sources", [])

    print(f"IP: {ip_address}")

    print(f"Fraud Score: {fraud_score}/100")

    print(f"Is Malicious: {is_malicious}")

    print(f"Blacklist Sources: {blacklist_sources}")

    # Evaluate IP purity

    if fraud_score <= 30:

        print("✅ IP Purity: Excellent (Green)")

        print("Suitable for account registration, accessing high-security websites")

    elif fraud_score <= 70:

        print("⚠️ IP Purity: Medium (Yellow)")

        print("May encounter CAPTCHA occasionally, normal browsing is fine")

    else:

        print("❌ IP Purity: Poor (Red)")

        print("May be marked as malicious proxy or bot")

    return data

# Example: Query current IP

current_ip = "185.220.101.6"  # Example IP (Tor exit node)

result = check_ip_reputation(current_ip)

```

**How to interpret the score?**

| Score Range | Level | Description |

|---------|------|------|

| 0-30 | Excellent (Green) | Very clean IP, suitable for account registration, browsing high-security websites |

| 30-70 | Medium (Yellow) | May encounter CAPTCHA occasionally, normal browsing is fine |

| 70-100 | Poor (Red) | May be marked as malicious proxy or bot, most websites will block |

**How to check IP address？** You can visit the IPing official website, enter the IP address to query the reputation score with one click.

---

## Step 2: Check "Origin" —— Real User or Machine?

Websites prefer "real users", not "server machines". In the second step, we need to check the IP's "origin" —— is it residential broadband (good) or datacenter (bad)?

**Why is "origin" important?**

- **Residential IP**: Assigned by ISP (Telecom, Unicom, Comcast, etc.) to home users, high trust level

- **Datacenter IP**: Assigned by cloud providers (AWS, DigitalOcean, Alibaba Cloud, etc.), easily identified as proxy or bot

If your IP is a datacenter IP, many websites will directly block or require additional verification.

**How to query IP geolocation and datacenter information using IPing?**

```python

import requests

def check_ip_origin(ip_address):

    """

    Query IP geolocation and type

    """

    api_url = "https://api.iping.com/v1/ip/geo"

    params = {

        "ip": ip_address,

        "api_key": "YOUR_API_KEY"

    }

    response = requests.get(api_url, params=params)

    data = response.json()

    # Parse geolocation and IP type

    country = data.get("country", "")

    city = data.get("city", "")

    isp = data.get("isp", "")

    ip_type = data.get("ip_type", "")  # residential or datacenter

    asn_type = data.get("asn_type", "")  # Determine type from ASN info

    print(f"IP: {ip_address}")

    print(f"Location: {country} {city}")

    print(f"ISP/ASN: {isp}")

    print(f"IP Type: {ip_type}")

    print(f"ASN Type: {asn_type}")

    # Evaluate IP "origin"

    if ip_type == "residential":

        print("✅ Residential IP (Real User)")

        print("High trust level, suitable for account registration")

    elif ip_type == "datacenter":

        print("⚠️ Datacenter IP (Machine)")

        print("May be identified as proxy, recommend changing IP")

    return data

# Example: Query IP

test_ip = "8.8.8.8"  # Google DNS (Datacenter IP)

result = check_ip_origin(test_ip)

```

**Street-level IP positioning** can help you precisely locate to street level,

**IPing's positioning accuracy options:**

- Country level (±1-5km)

- City level (±1-10km)

- Street level (±100-500m)

For IP purity detection, usually only country/city level positioning is needed. Street-level positioning is more used for:

- Anti-fraud (detect if login location is abnormal)

- Content localization (display local language/currency)

- Compliance requirements (some countries require storing user location)

IPing follows privacy protection principles and will not store or share your precise location information.

---

## Step 3: Check "Behavior" —— Is IP a Proxy?

In the final step, we need to check the IP's "behavior" —— is it hiding real identity? Is it using a proxy, VPN, or Tor?

**Why is proxy detection important?**

According to statistics, 78% of black/gray market attacks use proxy IPs to hide real identity. If your IP is detected as a proxy, then:

- E-commerce platforms may restrict orders

- Advertising platforms may reject ads

- Gaming platforms may ban accounts

- Financial services may freeze funds

**How to detect if an IP is a proxy using IPing?**

IPing provides professional proxy detection API that can identify:

- VPN

- Public proxy

- Residential proxy

- Tor exit nodes

- Datacenter proxy

```python

import requests

def check_if_proxy(ip_address):

    """

    Detect if IP is a proxy

    """

    api_url = "https://api.iping.com/v1/ip/proxy"

    params = {

        "ip": ip_address,

        "api_key": "YOUR_API_KEY"

    }

    response = requests.get(api_url, params=params)

    data = response.json()

    # Parse proxy detection results

    is_proxy = data.get("is_proxy", False)

    proxy_type = data.get("proxy_type", "")

    proxy_confidence = data.get("proxy_confidence", 0)

    print(f"IP: {ip_address}")

    print(f"Is Proxy: {is_proxy}")

    print(f"Proxy Type: {proxy_type}")

    print(f"Detection Confidence: {proxy_confidence}%")

    if is_proxy:

        print(f"⚠️ Warning: This IP is detected as {proxy_type}")

        print("Recommendation: Change IP or use IPing proxy detection API for regular monitoring")

    else:

        print("✅ This IP is not detected as a proxy")

    return data

# Example: Detect multiple IPs

ips_to_check = ["185.220.101.6", "103.152.220.13", "8.8.8.8"]

for ip in ips_to_check:

    print("-" * 50)

    check_if_proxy(ip)

```

**IP Data API Call Examples** —— The above code demonstrates how to call IPing's proxy detection API. You can integrate this code into your own applications to implement real-time IP risk control.

---

## Complete Self-Check Process: IPing IP Purity Detection Tool

Now, let's integrate the previous three steps to create a complete IP purity self-check tool:

```python

import requests

import json

class IPingPurityChecker:

    """

    IP Purity Self-Check Tool (Based on IPing API)

    """

    def __init__(self, api_key):

        self.api_key = api_key

        self.base_url = "https://api.iping.com/v1"

    def check_ip_purity(self, ip_address):

        """

        Complete IP purity detection (3 steps in 1)

        """

        print(f"\n{'='*60}")

        print(f"IP Purity Detection: {ip_address}")

        print(f"{'='*60}\n")

        # Step 1: Check "Criminal Record" (Reputation Score)

        print("[Step 1] Check 'Criminal Record' —— IP Reputation Score")

        reputation = self._check_reputation(ip_address)

        # Step 2: Check "Origin" (Geolocation and Type)

        print("\n[Step 2] Check 'Origin' —— IP Type and Geolocation")

        origin = self._check_origin(ip_address)

        # Step 3: Check "Behavior" (Proxy Detection)

        print("\n[Step 3] Check 'Behavior' —— Proxy Detection")

        proxy = self._check_proxy(ip_address)

        # Comprehensive Evaluation

        print("\n" + "="*60)

        print("Comprehensive Evaluation Result:")

        print("="*60)

        fraud_score = reputation.get("fraud_score", 0)

        ip_type = origin.get("ip_type", "")

        is_proxy = proxy.get("is_proxy", False)

        # Evaluation Logic

        if fraud_score <= 30 and ip_type == "residential" and not is_proxy:

            print("✅✅✅ IP Purity: Excellent")

            print("Recommended Use: Account registration, payment, high-security scenarios")

        elif fraud_score <= 70 and not is_proxy:

            print("⚠️⚠️ IP Purity: Medium")

            print("Recommended Use: Normal browsing, non-sensitive operations")

        else:

            print("❌❌❌ IP Purity: Poor")

            print("Recommendation: Change IP immediately, avoid using")

        return {

            "reputation": reputation,

            "origin": origin,

            "proxy": proxy,

            "overall_score": fraud_score

        }

    def _check_reputation(self, ip):

        """Step 1: Check Reputation"""

        url = f"{self.base_url}/ip/reputation"

        params = {"ip": ip, "api_key": self.api_key}

        response = requests.get(url, params=params)

        data = response.json()

        fraud_score = data.get("fraud_score", 0)

        print(f"  Fraud Score: {fraud_score}/100")

        print(f"  Blacklist Sources: {data.get('blacklist_sources', [])}")

        return data

    def _check_origin(self, ip):

        """Step 2: Check Geolocation and Type"""

        url = f"{self.base_url}/ip/geo"

        params = {"ip": ip, "api_key": self.api_key}

        response = requests.get(url, params=params)

        data = response.json()

        print(f"  Location: {data.get('country', '')} {data.get('city', '')}")

        print(f"  IP Type: {data.get('ip_type', '')}")

        return data

    def _check_proxy(self, ip):

        """Step 3: Check Proxy"""

        url = f"{self.base_url}/ip/proxy"

        params = {"ip": ip, "api_key": self.api_key}

        response = requests.get(url, params=params)

        data = response.json()

        print(f"  Is Proxy: {data.get('is_proxy', False)}")

        print(f"  Proxy Type: {data.get('proxy_type', '')}")

        return data

# Usage Example

if __name__ == "__main__":

    # Initialize detector

    checker = IPingPurityChecker(api_key="YOUR_API_KEY")

    # Query IP Location —— Get current IP

    current_ip = requests.get("https://api.iping.com/v1/ip/myip").json().get("ip")

    print(f"Current IP: {current_ip}")

    # Complete detection

    result = checker.check_ip_purity(current_ip)

```

---

## How to Maintain IP Purity?

After uncovering your IP's "dark history", how do you maintain IP purity?

**1. Regular Detection**

Use IPing API to regularly detect IP purity and promptly identify problems.

**2. Avoid Shared IPs**

Do not use public proxies or VPNs, as these IPs are often used by multiple people and easily marked.

**3. Follow Website Rules**

Do not frequently scrape data, do not batch register accounts, do not perform abnormal operations.

**4. Use IPing Monitoring Service**

IPing provides IP monitoring services that can:

- Real-time monitoring of IP reputation changes

- Email/Webhook alerts

- Historical record queries

- Batch IP management

---

## Query IP Location：Street-Level IP Positioning Privacy Considerations

**IPing's positioning accuracy options:**

- Country level (±1-5km)

- City level (±1-10km)

- Street level (±100-500m)

For IP purity detection, usually only country/city level positioning is needed. Street-level positioning is more used for:

- Anti-fraud (detect if login location is abnormal)

- Content localization (display local language/currency)

- Compliance requirements (some countries require storing user location)

IPing follows privacy protection principles and will not store or share your precise location information.

---

## Summary

IP purity is a key factor affecting network experience. Through the **3-step self-check method**:

1. Check "Criminal Record" —— IP reputation score

2. Check "Origin" —— IP type and geolocation

3. Check "Behavior" —— Proxy detection

You can quickly evaluate IP purity and take measures to keep your IP clean.

**IPing provides one-stop IP detection services:**

·  ✅ Simple IP query entry

·  ✅ Street-level precise positioning

·  ✅ Full Python API demo codes

·  ✅ Accurate proxy/VPN identification

·  ✅ Global real-time IP location lookup

Protect your network identity, start by detecting IP purity. Visit the IPing official website now to experience professional IP detection services!

About IPing

"Your IP address indicates: Frankfurt, Germany, Deutsche Telekom network." — When you query your IP location, have you ever wondered how that "Frankfurt" was determined — and how precise it really is?

In 2026, IP geolocation has evolved far beyond "showing you a rough city name." Technical benchmarks across major global cities confirm that premium IP positioning services now achieve over 99% accuracy in core metropolitan areas, with district-level errors under 1 kilometer and even street-level precision within acceptable margins for most commercial applications.

This represents a quantum leap — from city-level to street-level — and it comes with profound implications for privacy.

 Part 1: Why Was Your IP Location Always Wrong?

Before understanding street-level precision, it's worth addressing why so many people query IP location only to find results wildly off from their actual location.

Cause 1: The "Temporal Drift" of Dynamic IPs

Home broadband users typically receive dynamic IPs — every time you restart your router, your ISP assigns a new one. If the IP database hasn't been updated to reflect this change, your queried location may show the ISP's registered address for that IP pool (often a city-level headquarters), rather than where you actually are.

Cause 2: The "Big NAT Trap"

For mobile users, the situation is even murkier. A large proportion of smartphone traffic exits through carrier-grade NAT, meaning the external IP visible to the internet belongs to the operator's regional node — not the user's neighborhood. A phone in Hamburg using LTE may show an exit IP registered in Munich.

Cause 3: The "Database Version Gap"

The global IP space is enormous and constantly changing. Different databases — GeoIP, MaxMind, IPIP, IP-API — update at different frequencies and source from different channels, so the same IP can return different locations across different platforms.

 Part 2: Five Core Technologies Powering Street-Level IP Geolocation

Achieving street-level accuracy is far more complex than simply "upgrading a database." Here are the five dominant techniques behind modern precision IP positioning:

 1. Multi-Source Data Cross-Validation

No single source is perfect. Top-tier IP geolocation services aggregate data from multiple providers — IPIP, MaxMind GeoLite2, ipplus360, IP2Location, and others — applying weighted algorithms to reconcile discrepancies and minimize error margins.

 2. Triangulation Modeling

Borrowing from GPS technology, when a user is covered by multiple Wi-Fi access points or cell towers, signal strength and time-delay data can be used to triangulate an exact position. Advanced IP geolocation systems apply network topology analysis to construct virtual triangulation models based on where an IP block sits within the routing hierarchy.

 3. BGP Topology Analysis

Every IP block has a documented path through BGP (Border Gateway Protocol) routing tables. By analyzing AS (Autonomous System) numbers and network node distributions, geolocation engines can reverse-engineer the true geographic origin of an IP address with remarkable accuracy.

 4. Machine Learning Scenario Classification

Modern services use ML algorithms to classify IP addresses into 20+ scenario types: residential broadband, enterprise dedicated line, data center, mobile network, VPN exit, proxy server, and more. Each scenario type uses a tailored positioning strategy. A data center IP, for example, has a fundamentally different accuracy profile than a residential IP.

 5. User Feedback Loops

Leading IP data providers have built continuous improvement pipelines: when users voluntarily correct a location result (by pinning their actual position on a map), that feedback trains the next model iteration, driving a self-reinforcing accuracy improvement cycle.

 Part 3: Three Real-World Applications of Street-Level IP Geolocation

 Scenario 1: Financial Fraud Detection — "Legitimate Travel" or "Account Takeover"?

A Dutch bank's fraud detection system flags aCross-border transfer originating from an IP in Southeast Asia, while the account holder is registered in Frankfurt. The system needs to answer: is this a traveling employee using a VPN, or an account takeover from abroad?

Street-level IP geolocation provides the decisive signal. By querying the precise IP location — not just the country, but the district — the system can determine whether the IP is genuinely in Frankfurt (legitimate) or somewhere in Southeast Asia (high-risk). Combined with proxy/VPN detection, the model can escalate or clear the transaction with far greater confidence.

```python

 Python: Transaction risk assessment with iping API

import requests

def assess_crossborder_transaction(transaction_ip, account_holder_city):

    """

    Assess transaction risk by comparing registered location

    against street-level IP geolocation via iping API.

    """

    response = requests.get(

        "https://api.iping.com/ip/locate",

        params={

            "ip": transaction_ip,

            "precision": "street",     Request street-level accuracy

            "fields": "city,district,latitude,longitude,is_proxy,org_type"

        }

    )

    data = response.json()

    ip_city = data.get("city", "")

    ip_district = data.get("district", "")

    ip_country = data.get("country", "")

    is_proxy = data.get("is_proxy", False)

    org_type = data.get("org_type", "unknown")

    coords = f"{data.get('latitude', 0)}, {data.get('longitude', 0)}"

     Risk evaluation logic

    city_match = (ip_city.lower() == account_holder_city.lower())

    risk_flags = []

    if is_proxy:

        risk_flags.append(f"Proxy/VPN detected (org_type: {org_type})")

    if not city_match:

        risk_flags.append(

            f"Location mismatch: IP in {ip_city}, {ip_country} "

            f"vs registered address in {account_holder_city}"

        )

    risk_level = "HIGH" if (is_proxy or not city_match) else "LOW"

    return {

        "risk_level": risk_level,

        "ip_street_location": f"{ip_city}, {ip_district}",

        "coordinates": coords,

        "proxy_flag": is_proxy,

        "org_type": org_type,

        "risk_flags": risk_flags,

        "action": "MANUAL_REVIEW" if risk_level == "HIGH" else "AUTO_APPROVE"

    }

```

 Scenario 2: Hyperlocal Advertising — "Nearby" Actually Means Nearby

A London local services platform wants to push restaurant promotions to users within 500 meters of their current location. Traditional city-level IP data would push a user near Oxford Circus a deal for a restaurant 3 kilometers away in Canary Wharf.

Street-level IP geolocation changes everything. By resolving the user's IP to the Westminster district and Oxford Street specifically, the platform can deliver offers that are genuinely relevant — and see dramatically higher conversion rates.

```python

 Python: Hyperlocal ad targeting with iping precision lookup

import requests

from math import radians, sin, cos, sqrt, atan2

def haversine_distance(lat1, lon1, lat2, lon2):

    """Calculate distance in km between two coordinates."""

    R = 6371

    dlat = radians(lat2 - lat1)

    dlon = radians(lon2 - lon1)

    a = sin(dlat/2)2 + cos(radians(lat1))  cos(radians(lat2))  sin(dlon/2)2

    return R  2  atan2(sqrt(a), sqrt(1-a))

def filter_local_ads_with_iping(user_ip, ads_list, radius_km=0.5):

    """

    Filter ads by checking whether each business location

    falls within [radius_km] of the user's IP-derived street address.

    Uses iping for street-level IP geolocation.

    """

     Step 1: Get user's street-level IP location via iping API

    geo = requests.get(

        "https://api.iping.com/ip/locate",

        params={"ip": user_ip, "precision": "street"}

    ).json()

    user_lat = geo.get("latitude")

    user_lon = geo.get("longitude")

    user_address = f"{geo.get('city', '')} {geo.get('district', '')} {geo.get('street', '')}"

     Step 2: Filter ads by proximity

    local_ads = []

    for ad in ads_list:

        ad_lat = ad.get("latitude")

        ad_lon = ad.get("longitude")

        distance = haversine_distance(user_lat, user_lon, ad_lat, ad_lon)

        if distance <= radius_km:

            local_ads.append({

                "business": ad["name"],

                "distance_km": round(distance, 2),

                "address": ad["address"],

                "deal": ad["offer"]

            })

    return {

        "user_ip": user_ip,

        "user_address": user_address.strip(),

        "radius_km": radius_km,

        "matching_ads": local_ads,

        "total_found": len(local_ads)

    }

```

 Scenario 3: Cybercrime Investigation — From "Rough Area" to "Specific Block"

When Amsterdam police need to trace an online fraud suspect, traditional methods can only narrow the search to "which ISP owns this IP range" — potentially covering the entire Amsterdam metropolitan area.

Street-level IP geolocation changes the investigative calculus. By pinpointing the IP to a specific neighborhood or even a building block, investigators can focus resources dramatically more efficiently, shortening investigation timelines and improving case outcomes.

---

 Part 4: The Privacy Line — When Your IP Pins You to a Street

Street-level IP precision raises an uncomfortable question: where does the privacy boundary lie?

Proponents argue that precision geolocation is an indispensable tool for cybersecurity, financial fraud prevention, and law enforcement — protecting consumers from scams, catching hackers, and securing financial systems.

Critics counter that when an IP address can resolve to a specific street, individuals' online activity becomes tightly linked to their physical identity. Ad networks know which block you live on. Malicious actors can physically locate their targets.

This remains an unresolved tension.

Regulatory frameworks in most developed jurisdictions require explicit user consent for street-level IP tracking, and prohibit repurposing location data beyond stated consent. GDPR and CCPA both impose strict limitations. Yet enforcement remains challenging, and the technical capability continues to outpace regulatory speed.

For ordinary users, understanding "how to query IP address location" and recognizing what information it reveals is the first line of defense for your digital privacy.

---

 Part 5: Three Steps to Verify Your IP Location Accuracy

You can personally check whether your IP resolves correctly:

Step 1: Query your current IP

```bash

 Windows PowerShell

(Invoke-WebRequest -Uri "https://api.iping.com/ip/query" -UseBasicParsing).Content

```

Step 2: Call the precision API for street-level data

```python

import requests

def verify_my_ip_street_level():

    """

    Verify current IP street-level location using iping API.

    Demonstrates IP geolocation API usage with Street-level IP positioning support.

    """

    result = requests.get(

        "https://api.iping.com/ip/locate",

        params={

            "ip": "",               Empty = auto-detect current IP

            "precision": "street"   Request street-level precision

        }

    ).json()

    print(f"IP Address     : {result['ip']}")

    print(f"Country        : {result['country']}")

    print(f"City           : {result['city']}")

    print(f"District       : {result.get('district', 'N/A')}")

    print(f"Street         : {result.get('street', 'N/A')}")

    print(f"Coordinates    : {result['latitude']}, {result['longitude']}")

    print(f"ISP            : {result['isp']}")

    print(f"Organization   : {result.get('org', 'N/A')}")

    print(f"IP Type        : {result['org_type']}")

    print(f"Is Proxy       : {'Yes' if result['is_proxy'] else 'No'}")

verify_my_ip_street_level()

```

Step 3: Compare with your actual location

Open Google Maps, enter the returned latitude/longitude, and see how close it is. If the mismatch is significant, you may be behind a VPN (resolving to the VPN server's location), inside a carrier-grade NAT, or simply experiencing a database lag.

 Conclusion: Precision Is a Double-Edged Sword

The evolution from city-level to street-level IP geolocation is technically inevitable — and commercially valuable.

Our IP address is being scanned right now. What you don't know: over 100,000 active botnets are traversing every public IP range across the globe, hunting for vulnerable devices. From home routers to enterprise servers, your IP address—the "house number" of the digital world—is both the backbone of internet communication and the first target cyberattackers aim at.

 Part 1: Your IP Address is "Naked": What Does It Leak?

Many people assume an IP address is just a string of irrelevant numbers. In reality, the moment you visit any website, your IP address is fully exposed. With street-level IP geolocation, modern IP lookup services can pinpoint your approximate location—down to the city, district, and even neighborhood.

This isn't science fiction. Services like iping integrate data from hundreds of global ISPs to map IP addresses to real-world locations. For ordinary users, this means your query IP location could be obtained by strangers. For businesses, it means attackers have already completed their reconnaissance before launching any operation.

The IP leak damage chain can be summarized in three steps:

Step 1: Information Gathering. Attackers use search engines, IoT search platforms like Shodan, or direct IP scanning to collect basic intelligence: target IP ranges, open ports, running services. This step is entirely passive—most users don't even notice.

Step 2: Profiling Correlation. Cross-referencing IP data with public sources—social media posting IPs, corporate website server IPs, VPN exit IPs—attackers build an organizational map. No system breach required. They can determine "which cloud service this company uses" from public information alone.

Step 3: Precision Strike. Based on intelligence from the previous two steps, attackers target the weakest link: brute-forcing SSH ports, exploiting VPN vulnerabilities, or sending phishing emails to corporate accounts. The easier it is to find out how to query an IP address, the lower the attacker's reconnaissance cost.

 Part 2: The IP Attack Panorama: What Cards Do Attackers Hold?

 1. Botnet Scanning

Attackers first use globally distributed botnets to automate port scanning and service fingerprinting across target IP ranges. Common scanning targets:

- SSH (Port 22): Default entry point for Linux servers

- RDP (Port 3389): High-frequency target for Windows remote desktop attacks

- IoT Devices (Ports 8080/8443): Cameras, routers, industrial control systems

After scanning, attackers have a "target list" containing thousands of IPs with weak passwords or unpatched vulnerabilities.

 2. DDoS Attacks

Once attackers control enough bot nodes, they launch Distributed Denial of Service attacks by flooding the target IP with simultaneous requests from thousands of compromised devices, exhausting bandwidth or computing resources until normal users are locked out.

In recent years, reflection amplification attacks have become the dominant method. Attackers spoof the victim's IP and send requests to open DNS, NTP, and other servers with amplification factors—sometimes dozens or even hundreds of times larger—flooding the target IP until it collapses completely.

 3. Social Engineering Attacks

Once attackers have a target's IP geolocation, they can impersonate local ISP support to send phishing emails, or infer work hours based on the target's timezone to strike at the most opportune moment. Combined with social engineering database data, attackers can trace a single IP address to an actual individual.

 Part 3: The Defense System: Six Layers of IP Security

 Layer 1: Concealment and Obfuscation

NAT (Network Address Translation) is the first line of defense for home and enterprise networks. By mapping multiple private IPs to a single public IP, NAT limits external direct access to internal devices. Printers, employee computers, and other devices inside an enterprise network remain invisible to the public internet.

VPN (Virtual Private Network) replaces the user's real IP with the VPN server's exit IP through an encrypted tunnel. The outside world sees only the VPN server's IP—not the user's actual address.

 Layer 2: IP Risk Profiling

With professional IP lookup services like iping, enterprises can instantly check whether an IP uses a proxy, is a datacenter IP, or has a mismatch between geolocation and registration data.

Here is a foundational risk detection example using the iping API:

`python

import requests

def check_ip_risk(ip_address):

     iping API endpoint

    url = f"https://api.iping.com/ip/query?ip={ip_address}"

    response = requests.get(url)

    data = response.json()

     Core checks: is the IP a proxy?

    is_proxy = data.get('is_proxy', False)

    is_datacenter = data.get('is_datacenter', False)

    country = data.get('country', 'Unknown')

    city = data.get('city', 'Unknown')

    risk_score = data.get('risk_score', 0)

    result = {

        'ip': ip_address,

        'location': f"{country} / {city}",

        'is_proxy': is_proxy,

        'is_datacenter': is_datacenter,

        'risk_level': 'HIGH' if risk_score > 70 else 'MEDIUM' if risk_score > 40 else 'LOW',

        'recommendation': 'BLOCK' if is_proxy and risk_score > 70 else 'CHALLENGE' if risk_score > 40 else 'ALLOW'

    }

    return result

 Example: check if IP is a proxy and get risk score

test_ip = "203.0.113.42"

result = check_ip_risk(test_ip)

print(f"IP: {result['ip']}")

print(f"Location: {result['location']}")

print(f"Proxy Risk: {'Yes' if result['is_proxy'] else 'No'}")

print(f"Datacenter IP: {'Yes' if result['is_datacenter'] else 'No'}")

print(f"Risk Level: {result['risk_level']}")

print(f"Recommended Action: {result['recommendation']}")

`

This demonstrates how to use iping's IP data API call example to evaluate each visitor's IP risk profile in real time. IPs from high-risk proxy pools or datacenters can be blocked outright or subjected to additional verification.

 Layer 3: Intelligent Threat Intelligence Integration

Modern enterprise Security Operations Centers (SOC) subscribe to third-party threat intelligence feeds that automatically update malicious IP blacklists. Here is an enterprise-grade IP threat intelligence detection framework:

`python

from datetime import datetime

from collections import defaultdict

class IPThreatIntelligenceSystem:

    def __init__(self):

        self.blacklist = set()   Malicious IP blacklist

        self.whitelist = set()   Trusted IP whitelist

        self.threat_history = defaultdict(list)   Threat history

        self.threshold = 5   Alert trigger threshold

    def query_iping_intelligence(self, ip):

        import requests

        url = f"https://api.iping.com/ip/intelligence?ip={ip}"

        response = requests.get(url)

        return response.json()

    def check_ip(self, ip):

         Step 1: Whitelist check

        if ip in self.whitelist:

            return {'status': 'ALLOW', 'reason': 'Whitelisted'}

         Step 2: Blacklist check

        if ip in self.blacklist:

            return {'status': 'BLOCK', 'reason': 'Blacklisted'}

         Step 3: Deep profiling via iping

        ip_data = self.query_iping_intelligence(ip)

        risk_factors = []

        if ip_data.get('is_proxy'):

            risk_factors.append('PROXY')

        if ip_data.get('is_datacenter'):

            risk_factors.append('DATACENTER')

        if ip_data.get('country') != ip_data.get('isp_country'):

            risk_factors.append('LOCATION_MISMATCH')

         Check attack history

        if ip in self.threat_history:

            attack_count = len(self.threat_history[ip])

            if attack_count >= self.threshold:

                risk_factors.append(f'HISTORY({attack_count} attacks)')

        risk_score = len(risk_factors)  25

        if risk_score >= 75:

            return {'status': 'BLOCK', 'risk_score': risk_score, 'factors': risk_factors}

        elif risk_score >= 50:

            return {'status': 'CHALLENGE', 'risk_score': risk_score, 'factors': risk_factors}

        else:

            return {'status': 'ALLOW', 'risk_score': risk_score, 'factors': risk_factors}

    def add_to_blacklist(self, ip, threat_type='MANUAL'):

        self.blacklist.add(ip)

        self.threat_history[ip].append({

            'timestamp': datetime.now(),

            'type': threat_type

        })

        print(f"[ALERT] IP {ip} added to blacklist. Threat type: {threat_type}")

 Usage example

system = IPThreatIntelligenceSystem()

test_ips = ["198.51.100.23", "203.0.113.42", "192.168.1.1"]

for ip in test_ips:

    result = system.check_ip(ip)

    print(f"IP: {ip} | Status: {result['status']} | Risk Score: {result.get('risk_score', 0)}")

`

This system performs multi-dimensional profiling—proxy labels, datacenter tags, geolocation anomalies, attack history—to upgrade from "passive blocking" to "active hunting."

 Layer 4: Intrusion Detection and Automated Response

IDS/IPS (Intrusion Detection/Prevention Systems) analyze IP behavioral patterns in network traffic in real time. When a single IP scans multiple ports in a short period, or when an IP's request frequency spikes abnormally, IPS can automatically trigger blocking.

Enterprises can integrate IDS/IPS with iping's IP profiling data to batch-block known high-risk datacenter IP ranges and whitelist trusted residential IP ranges, dramatically reducing false positives.

 Layer 5: Geolocation Filtering

IP-based geolocation filtering is becoming a standard enterprise security configuration:

- Financial services sites block foreign IPs entirely, intercepting offshore proxy IPs

- Gaming platforms verify IP geolocation during account registration, flagging mismatches for manual review

- Corporate intranet admin panels restrict access to IPs from the company's city (e.g., London's financial district)

iping supports street-level IP geolocation accuracy, allowing administrators to set access whitelists down to the city or district level.

 Layer 6: DDoS Traffic Scrubbing

When attack traffic exceeds blocking capacity, operator-grade or cloud-based DDoS scrubbing centers redirect all traffic for filtering, removing attack packets before returning clean traffic to the target.

Modern scrubbing centers use IP profiling technology to distinguish between legitimate users and botnet nodes. Requests from the same datacenter IP range, even if masquerading as normal users, are easier to identify and block.

 Part 4: Real Case Study: From One IP to Full System Compromise

A London-based technology company's operations engineer, scanning their own server IP ranges with Shodan, discovered an unpatched vulnerability on a publicly exposed test server. Even more alarming: that server's IP was already on a dark web "assets for sale" list. Attackers had found it before the company did.

Sequence of events:

1. Attackers located the target server via exposed IP

2. Exploited the unpatched vulnerability to gain server access

3. Pivoted through internal IPs to compromise the core database

4. Exfiltrated over 12 GB of customer data

Post-incident analysis showed: had the company used iping for inbound IP profiling, the attacker's IP would have been flagged before the incident as "datacenter IP + high-frequency scanning behavior"—enabling interception at the first hop.

 Part 5: What Can Ordinary Users Do?

Even without coding skills, you can dramatically reduce IP exposure risk:

- Enable your firewall: Windows and macOS include built-in firewalls—turn on "block all incoming connections"

- Use a VPN: On public Wi-Fi, a trusted VPN hides your real IP

- Avoid exposing services directly: Don't expose remote desktop, file sharing, or other personal services to the public internet

- Regularly check IP exposure: Use iping to look up your public IP and understand what information it reveals

- Check router settings: Ensure the router firewall is enabled and UPnP is configured only as needed

 Part 6: Conclusion—IP Security is an Escalating Arms Race

Your IP address—the foundation of internet communication—directly determines your network security posture. From individual users to large enterprises, we all share one reality: your IP is being scanned, profiled, and tracked.

In November 2025, the Security Operations Center (SOC) of a London-based fintech company received an unusual alert: a remote-working quantitative analyst was attempting to log into the firm's core trading system from a residential IP in Amsterdam, Netherlands. However, according to HR records, the analyst lived in Slough, Berkshire, and hadn't left the UK in the past six months. The security team immediately initiated the incident response protocol, forced the account to log out, and launched a deep investigation. What they discovered: an attacker had obtained the analyst's credentials through a phishing email and was attempting to bypass the final line of defense—MFA (Multi-Factor Authentication). And it was precisely the IP address geolocation data that built a second layer of defense outside the authentication flow, blocking this near-successful breach.

This is not an isolated case. With remote work now fully mainstream, the enterprise security perimeter has long since expanded from the office firewall to every corner of the globe. The traditional "connect first, trust later" model is being completely overturned, and IP address querying is becoming an indispensable infrastructure in the Zero Trust security architecture.

 The Security Predicament in the Remote Work Era

In traditional network architecture, an enterprise's security boundary was defined by the firewall. Once an employee was inside the "internal network," they were implicitly granted access to various system resources. This model worked well when employees worked primarily from the office, but as remote work, BYOD (Bring Your Own Device), and multi-branch organizations became the norm, the security boundary had completely dissolved.

Numbers tell the story. According to the "2025 Remote Work Security Report" published by global cybersecurity leader Cato Networks, 67% of enterprise data breach incidents are directly related to remote access environments, with more than 40% of initial intrusions originating from home networks or public Wi-Fi. More critically, traditional VPNs protect the connection but often neglect continuous verification—once an employee passes authentication, the system implicitly trusts their behavior, allowing them to move laterally across the network.

Thus, the Zero Trust security architecture based on "Never Trust, Always Verify"—after more than a decade of dormancy—has finally taken center stage in enterprise security.

 Zero Trust Architecture: IP Becomes the Key Factor in "Trust Scores"

The core concept of Zero Trust is straightforward: every access request, regardless of whether it originates from inside or outside the network, must undergo identity verification and authorization assessment. Within this framework, IP address querying plays an irreplaceable role—it provides critical information about the user's geographic location, network ownership, and whether the IP belongs to a proxy or data center. This information directly determines the "trust score" of the access request.

 Scenario One: Dynamic Access Control—IP Decides What You Can See

In a Zero Trust architecture, a Paris-based marketing employee attempts to access the company's CRM system from their home. The security policy is configured as follows: only residential IPs from EU regions are permitted, and the IP must not belong to a data center or proxy service.

The employee's laptop sends a login request; the system extracts the IP address (assumed to be a French Orange ISP residential IP) and queries it through iping's IP data interface call example. The returned result shows: the IP is located in the Île-de-France region of France, with street-level IP geolocation accuracy of 98.3%, identified as a residential IP with no proxy or VPN usage. The system judges the IP reputation score as 85 out of 100, within the trusted range, and allows the login request.

However, if the employee is working from a co-working space using a data center IP, or their VPN happens to use an exit node from a Dutch data center, iping immediately identifies this IP as non-residential proxy and tags it as "high risk" in the risk profile. The system can choose to deny access or trigger secondary verification (such as requiring a push notification confirmation to the phone), blocking the potential risk before it materializes.

 Scenario Two: Anomaly Detection—IP Tells You "This Is Not You"

Even if an attacker successfully passes identity verification, proxy/VPN detection for IP addresses can still play a critical role. Suppose an attacker logs in from a data center IP in an Eastern European country, while the target user's habitual login IP is in London. Even with the correct credentials, the system can still detect the anomaly through IP geolocation inconsistency.

Specifically, the security team can configure a policy: if an account switches to a login IP on a different continent within 24 hours (for example, from the UK to Russia with less than a 2-hour interval), immediately trigger account lockout and notify the security team. The logic here is simple: a normal commuter in London cannot travel from the UK to Russia within two hours—this type of switch almost certainly indicates stolen credentials.

iping's IP location query function supports high-frequency real-time queries, allowing the security platform to compare each login's IP data against the user's historical records and generate behavioral anomaly alerts. According to Gartner's estimates, this IP geolocation-based anomaly detection can reduce average losses after account takeover by 40% to 60%, as it dramatically shortens the time window between intrusion and detection.

 Scenario Three: Fine-Grained Access Control—IP Decides What You Can Do

Another core principle of Zero Trust architecture is Least Privilege Access. In this context, enterprises can assign differentiated access permissions to employees based on their IP source.

For example, when a California-based developer accesses the code repository from their home IP, the system identifies this as a US Verizon residential IP with a score of 92, granting full code read/write permissions. But if the same employee logs in from a VPN node (identified as a data center IP), the system can automatically downgrade their permissions to read-only and log their operations for audit purposes.

This IP geolocation-based dynamic permission adjustment enhances access security granularity to an unprecedented level without adding operational burden to users.

 Technical Implementation: Building a Zero Trust IP Verification Layer with iping

Below is a Python code example implementing remote work Zero Trust verification based on iping's IP data interface, demonstrating IP data interface call example in a real production environment:

```python

import requests

import json

from datetime import datetime, timedelta

 iping API configuration

IPING_API_KEY = "your_iping_api_key_here"

IPING_ENDPOINT = "https://api.iping.com/v1/query"

def check_ip_trust_score(ip_address, user_id):

    """

    Comprehensive IP address trust scoring for Zero Trust decision engine.

    Returns score (0-100) and detailed risk report.

    """

     Call iping API - querying IP address

    response = requests.get(

        IPING_ENDPOINT,

        params={"ip": ip_address, "key": IPING_API_KEY, "fields": "all"},

        timeout=5

    )

    if response.status_code != 200:

        return {"score": 0, "error": "API request failed"}

    data = response.json()

     Base score: starts at 100

    score = 100

    risk_factors = []

     Check 1: Is it a proxy/VPN? (Is the IP a proxy?)

    if data.get("is_proxy") or data.get("is_vpn"):

        score -= 40

        risk_factors.append("Proxy or VPN service detected")

     Check 2: Is it a data center IP?

    if data.get("ip_type") == "datacenter":

        score -= 25

        risk_factors.append("Data center IP - permissions auto-demoted")

     Check 3: Street-level IP geolocation - querying IP location

    country = data.get("country", "")

    region = data.get("region", "")

    city = data.get("city", "")

     Check 4: Does IP geolocation match user's registered location?

    user_profile = get_user_registered_location(user_id)   Assumed from database

    if user_profile and country != user_profile["country"]:

        score -= 30

        risk_factors.append(f"IP location ({country}) does not match registered location ({user_profile['country']})")

     Check 5: Historical behavior analysis

    recent_ips = get_user_recent_login_ips(user_id, days=30)

    if recent_ips:

         If current IP is significantly different from past 30-day login records, deduct points

        if ip_address not in recent_ips:

            score -= 15

            risk_factors.append("New IP address never seen before")

         Detect cross-continental login within short time

        for past_ip in recent_ips:

            past_data = get_ip_info_cached(past_ip)

            if past_data and data.get("country") != past_data.get("country"):

                days_since = get_days_since_ip(past_ip)

                if days_since < 1:   Cross-country login within 24 hours

                    score -= 20

                    risk_factors.append("Cross-country login within 24 hours (HIGH RISK)")

    return {

        "score": max(0, score),

        "risk_level": "low" if score >= 70 else "medium" if score >= 40 else "high",

        "risk_factors": risk_factors,

        "ip_details": {

            "country": country,

            "region": region,

            "city": city,

            "isp": data.get("isp", "unknown"),

            "ip_type": data.get("ip_type", "unknown"),

            "is_proxy": data.get("is_proxy", False),

            "is_vpn": data.get("is_vpn", False),

            "is_datacenter": data.get("ip_type") == "datacenter"

        }

    }

def zero_trust_access_decision(ip_address, user_id, resource_level="normal"):

    """

    Zero Trust Access Decision Engine: Determines access permission level based on IP trust score.

    """

    trust = check_ip_trust_score(ip_address, user_id)

     Permission tier rules

    if trust["score"] >= 80:

        access_level = "full"

        print(f"[ALLOW] Trust score {trust['score']} - Full access granted")

    elif trust["score"] >= 50:

        access_level = "limited"

        print(f"[LIMIT] Trust score {trust['score']} - Limited to read-only access")

    else:

        access_level = "blocked"

        print(f"[BLOCK] Trust score {trust['score']} - Access denied, alert triggered")

     Log audit trail

    log_access_attempt(

        user_id=user_id,

        ip_address=ip_address,

        trust_score=trust["score"],

        access_level=access_level,

        risk_factors=trust["risk_factors"],

        ip_location=f"{trust['ip_details']['country']}/{trust['ip_details']['city']}",

        timestamp=datetime.now().isoformat()

    )

    return {

        "allowed": access_level != "blocked",

        "access_level": access_level,

        "require_mfa": trust["score"] < 80,

        "risk_factors": trust["risk_factors"],

        "trust_score": trust["score"]

    }

```

This code demonstrates the practical application of street-level IP geolocation in a Zero Trust architecture. Through iping's API, enterprises can quickly evaluate an IP's trust score on every access request and decide whether to allow, limit, or block. This decision can be made in milliseconds with near-zero impact on user experience.

 From VPN to Zero Trust: The Evolution of IP Verification's Role

Looking back at the evolution of remote work security, the role of IP in the security framework has gone through three major transformations:

Phase One: The VPN Era (2000s-2018)—IP was the "access card." Possessing a company-assigned IP address meant access to the internal network. Security policies were organized around IP address ranges, with the IP itself representing permission. This model was effective when employees were few and devices were controllable, but as mobile work became widespread, the IP address itself could no longer represent a trusted identity.

Phase Two: The Identity-First Era (2018-2023)—IP became supplementary information. Multi-factor authentication (MFA) became the mainstream security practice, and identity verification began to incorporate device fingerprints and behavioral analysis. IP data was primarily used for compliance auditing and post-incident forensics, not real-time decision-making.

Phase Three: The Zero Trust Era (2023–present)—IP becomes the key dimension of "trust scoring." In a Zero Trust architecture, every access request requires comprehensive evaluation of multiple factors: identity, device, network environment, and IP geolocation. iping's street-level IP geolocation and real-time proxy detection capabilities have elevated IP data from "reference information" to "decision-making basis."

 Enterprise Zero Trust IP Verification Practices

Across Europe, several major financial institutions have begun integrating iping into their Zero Trust access platforms. Take a German commercial bank as an example: they deployed an iping-based IP real-time verification system for remote work scenarios, processing approximately 1.2 million login requests per month, with about 3.5% of requests identified as high-risk and triggering secondary verification or direct blocking.

The bank's security director stated: "Before introducing iping's street-level IP geolocation, we could only rely on traditional VPN logs for post-incident analysis. Now, we can determine whether an IP is trustworthy the moment a login request arrives. This has shortened average response time from several hours to milliseconds."

Another noteworthy case comes from the European Commission. In 2025, the European Union Agency for Cybersecurity (ENISA), in its published "Zero Trust Implementation Guide," specifically highlighted the value of IP address querying in remote work security, recommending that member state government agencies incorporate IP data interface call examples into the standardization components of Zero Trust security architecture.

 Recommendations: How Enterprises Can Build an IP-Based Zero Trust Defense

For enterprises deploying or planning to deploy a Zero Trust architecture, here are core recommendations from industry practice:

First, incorporate IP verification into the core access decision process. Don't limit IP address data to auditing or logging—make it directly participate in real-time access control decisions. Through iping's API, every login request can quickly receive a trust score for that IP.

Second, enable "proxy/VPN detection" functionality. In remote work scenarios, employees using VPNs is common, but this also provides cover for attackers. Through iping's is the IP a proxy detection, the security team can configure stricter verification processes for access requests using proxy IPs without affecting normal business operations.

Third, establish user IP behavior baselines. Each user's login IPs follow a pattern—typically concentrated in a few cities or regions. By continuously recording and analyzing these behaviors through iping's street-level IP geolocation, the system can more precisely identify abnormal login behaviors.

Fourth, cross-timezone IP switches must trigger alerts. If a London employee's IP switches from the UK to Australia within 8 hours, the system should immediately alert. This type of cross-continental switch is virtually impossible for the person themselves—it must indicate stolen credentials or session hijacking.

Fifth, integrate IP data with SIEM/SOAR platforms. iping's IP query results can be output to Security Information and Event Management (SIEM) or Security Orchestration, Automation, and Response (SOAR) platforms, combining with threat intelligence, EDR, and other data sources to build more comprehensive security situational awareness.

 Conclusion: IP Address Is the "First Sensor" of Zero Trust

In a Zero Trust world, no single signal can independently determine trust—but the IP address is the "first sensor" that every access request must pass through. It requires no user cooperation, no additional software installation, and every network request automatically carries IP information. Leveraging iping's street-level IP geolocation and real-time proxy detection capabilities, enterprises can convert IP data into a trust factor for continuous verification at extremely low cost.

The Technical War Behind a "Century-Livestream"

In April 2025, a top influencer's live commerce stream hit $230 million in sales. However, behind this spectacle, the platform's risk control system intercepted over 1.4 million abnormal access requests in the same timeframe—most from **IP proxy pools**, designed to simulate real users for order fraud.

This is not an isolated case.

The global live streaming market has surpassed $250 billion (with over 50 million daily active streamers), but beneath the prosperity runs a dangerous undercurrent: fake viewers, gift boosting, comment spam, bonus abuse... Every live streaming platform wages an endless battle against cheaters exploiting **IP address** vulnerabilities.

In this battle, **how to query IP addresses**, **whether IP is a proxy**, **query IP location**—these basic capabilities are becoming core competitive advantages for streaming platforms.

## The "IP World" of Live Streaming

### 1. Comment Management: Who Is Speaking?

When you see "666" or "Buy it!" comments in a livestream, you probably don't know: behind every comment, the platform conducts real-time **IP geolocation** verification.

**Common tactics of malicious users:**

- **IP proxy pools**: Using large numbers of proxy IPs to post comments in rotation, bypassing single-IP posting frequency limits

- **Second-dial IPs**: Exploiting dynamic IP characteristics of residential broadband to switch IPs within seconds, evading bans

- **Botnets**: Collections of controlled devices acting collectively to create false hype

Data from a leading streaming platform in 2024 shows: through **IP data interface call examples** analysis, the system identified:

- Daily abnormal comment requests: 18 million

- VPN/proxy proportion: 67%

- Successful interception rate: 99.2%

**How iping helps comment management:**

```python

import requests

def check_user_ip_risk(ip_address, api_key):

    """

    Use iping to query IP address risk level

    For real-time risk control before comment posting

    """

    response = requests.get(

        "https://api.iping.com/v1/risk",

        params={"ip": ip_address, "apikey": api_key}

    )

    data = response.json()

    return {

        "ip": ip_address,

        "is_proxy": data["security"]["is_proxy"],

        "is_vpn": data["security"]["is_vpn"],

        "is_tor": data["security"]["is_tor"],

        "is_datacenter": data["security"]["is_datacenter"],

        "risk_score": data["risk"]["score"],

        "country": data["geolocation"]["country"],

        "city": data["geolocation"]["city"]

    }

def filter_danmu_with_iping(danmu_list, api_key):

    """

    Batch filter suspicious IPs in comments

    """

    filtered = []

    blocked = []

    for danmu in danmu_list:

        ip_info = check_user_ip_risk(danmu["ip"], api_key)

        # High-risk IP directly blocked

        if ip_info["risk_score"] > 70 or ip_info["is_proxy"]:

            blocked.append({

                "content": danmu["content"],

                "reason": "High-risk proxy IP",

                "ip": ip_address,

                "location": f"{ip_info['country']}-{ip_info['city']}"

            })

        else:

            filtered.append(danmu)

    return filtered, blocked

# Example: Filter 100 comments

api_key = "your_iping_api_key"

danmu_list = [{"ip": f"185.220.101.{x}", "content": "spam ad"} for x in range(100)]

filtered, blocked = filter_danmu_with_iping(danmu_list, api_key)

print(f"Passed comments: {len(filtered)}")

print(f"Blocked comments: {len(blocked)}")

```

### 2. Live Commerce: Who Is Paying?

In 2025, live commerce accounts for 35% of total e-commerce sales. But high returns come with high risks: soaring return rates, order fraud, bonus abusers running wild.

**Typical fraud scenarios:**

- **Order boosting for cashout**: Teams use batch IPs to register fake accounts, inflate sales, then cash out platform subsidies

- **Bonus hunters**: Using IP proxies to impersonate new users, repeatedly claiming new user coupons

- **Malicious returns**: Same shipping IP used for multiple returns, or shipping address inconsistent with IP location

Data from a major e-commerce platform's 2024 Double 11 shows:

- 18% of live commerce orders had IP correlation anomalies

- **Street-level IP positioning** analysis revealed 5% of orders had shipping addresses over 500km from IP location

- Through IP proxy detection, identified and blocked 2.3 million "bonus hunter" accounts

**iping builds live commerce risk control:**

```python

import requests

def verify_livestream_order_with_iping(order_info, api_key):

    """

    Use iping to verify authenticity of live commerce orders

    """

    user_ip = order_info["user_ip"]

    shipping_address = order_info["shipping_address"]

    claimed_location = order_info["claimed_location"]

    # 1. Query user IP location

    ip_response = requests.get(

        "https://api.iping.com/v1/query",

        params={"ip": user_ip, "apikey": api_key}

    )

    ip_data = ip_response.json()

    # 2. Detect if it's a proxy IP

    risk_response = requests.get(

        "https://api.iping.com/v1/security",

        params={"ip": user_ip, "apikey": api_key}

    )

    risk_data = risk_response.json()

    # 3. Risk assessment

    risk_factors = []

    risk_score = 0

    # IP inconsistent with claimed location

    if ip_data["city"] != claimed_location.get("city"):

        risk_factors.append(f"IP in {ip_data['city']}, user claims {claimed_location['city']}")

        risk_score += 30

    # IP inconsistent with shipping address

    if shipping_address.get("country") != ip_data["country"]:

        risk_factors.append(f"Shipping address {shipping_address['country']} inconsistent with IP location {ip_data['country']}")

        risk_score += 40

    # Using proxy IP

    if risk_data.get("is_proxy") or risk_data.get("is_vpn"):

        risk_factors.append("VPN or proxy IP detected")

        risk_score += 35

    # Datacenter IP (common in batch registration)

    if risk_data.get("is_datacenter"):

        risk_factors.append("Datacenter IP, suspected batch accounts")

        risk_score += 25

    return {

        "risk_score": min(risk_score, 100),

        "risk_level": "High" if risk_score >= 60 else "Medium" if risk_score >= 30 else "Low",

        "risk_factors": risk_factors,

        "ip_location": f"{ip_data['country']}-{ip_data['city']}",

        "recommend_action": "Manual Review" if risk_score >= 50 else "Approve"

    }

# Example: Verify a live commerce order

order = {

    "user_ip": "45.77.193.128",

    "shipping_address": {"country": "United States", "city": "New York"},

    "claimed_location": {"country": "United States", "city": "Los Angeles"}

}

result = verify_livestream_order_with_iping(order, "your_api_key")

print(f"Risk Score: {result['risk_score']}/100")

print(f"Risk Level: {result['risk_level']}")

print(f"Recommended Action: {result['recommend_action']}")

```

### 3. Multi-Account Detection: Who Is Bulk Operating?

Streaming guilds, agency operations, MCN companies... It's normal for one organization to manage dozens or hundreds of accounts. But the question is: which are normal operations, and which are batch-operated "sock puppet accounts"?

**IP clues for multi-account correlation:**

- Same IP range (e.g., 93.238.x.x) registered multiple accounts

- **Query IP location** reveals logins concentrated in the same city

- Login times highly coincident (e.g., simultaneously active at 2-4 AM)

- Obvious proxy IP characteristics (same IP logged into dozens of accounts in a short time)

**iping multi-account detection solution:**

```python

import requests

from collections import Counter

def detect_multi_accounts_with_iping(account_list, api_key):

    """

    Use iping to analyze account list and detect multi-account correlation risks

    """

    ip_analysis = {}

    # 1. Query IP information for each account

    for account in account_list:

        ip = account["last_login_ip"]

        # Call iping to query IP details

        response = requests.get(

            "https://api.iping.com/v1/query",

            params={"ip": ip, "apikey": api_key}

        )

        data = response.json()

        ip_analysis[account["account_id"]] = {

            "ip": ip,

            "country": data["country"],

            "region": data["region"],

            "city": data["city"],

            "isp": data["isp"],

            "account_id": account["account_id"]

        }

    # 2. IP clustering analysis

    city_groups = Counter()

    isp_groups = Counter()

    ip_prefix_groups = Counter()

    for account_id, info in ip_analysis.items():

        city_groups[info["city"]] += 1

        isp_groups[info["isp"]] += 1

        # Extract IP prefix (e.g., 93.238.x.x)

        ip_prefix = ".".join(info["ip"].split(".")[:2])

        ip_prefix_groups[ip_prefix] += 1

    # 3. Generate correlation report

    suspicious_groups = []

    # Same city exceeding 5 accounts

    for city, count in city_groups.items():

        if count >= 5:

            accounts = [a["account_id"] for a in ip_analysis.values() if a["city"] == city]

            suspicious_groups.append({

                "type": "Same City Cluster",

                "location": city,

                "account_count": count,

                "account_ids": accounts

            })

    # Same ISP exceeding 10 accounts

    for isp, count in isp_groups.items():

        if count >= 10:

            accounts = [a["account_id"] for a in ip_analysis.values() if a["isp"] == isp]

            suspicious_groups.append({

                "type": "Same ISP Cluster",

                "isp": isp,

                "account_count": count,

                "account_ids": accounts

            })

    return {

        "total_accounts": len(account_list),

        "suspicious_groups": suspicious_groups,

        "recommendation": "Manual Review" if len(suspicious_groups) > 0 else "No Anomalies"

    }

# Example: Detect 100 accounts

accounts = [{"account_id": f"acc_{i}", "last_login_ip": f"93.238.{i//256}.{i%256}"} for i in range(100)]

result = detect_multi_accounts_with_iping(accounts, "your_api_key")

print(f"Total accounts: {result['total_accounts']}")

print(f"Suspicious clusters: {len(result['suspicious_groups'])}")

```

### 4. Streamers and Content Distribution: Who Should See What?

**Street-level IP positioning** serves not only risk control but also content distribution:

- **Geo-restricted content**: Some streams only available to specific regions (e.g., sports broadcasting copyright restrictions)

- **CDN optimization**: Select optimal CDN nodes based on **query IP location**, reducing latency

- **Localized recommendations**: Recommend local popular streamers or events based on user IP

```python

def get_optimal_cdn_with_iping(user_ip, api_key):

    """

    Use iping to query user IP and select optimal CDN node

    """

    response = requests.get(

        "https://api.iping.com/v1/query",

        params={"ip": user_ip, "apikey": api_key}

    )

    data = response.json()

    # CDN node mapping (example)

    cdn_nodes = {

        "Los Angeles": "cdn-lax.iping.com",

        "New York": "cdn-nyc.iping.com",

        "Frankfurt": "cdn-fra.iping.com",

        "Tokyo": "cdn-tky.iping.com",

        "Singapore": "cdn-sgp.iping.com"

    }

    # Match nearest CDN node

    city = data["city"]

    cdn = cdn_nodes.get(city, "cdn-fra.iping.com")  # Default to European node

    return {

        "user_ip": user_ip,

        "user_location": f"{data['country']}-{data['city']}",

        "optimal_cdn": cdn,

        "latency_estimate": "20ms" if city in cdn_nodes else "80ms"

    }

```

## Live Streaming Platform IP Security Overview

| Application Scenario | Core Need | iping Solution |

|--------------------|-----------|----------------|

| Comment Management | Identify proxy IP posting | Real-time IP proxy detection, block high-risk IPs |

| Live Commerce | Anti-fraud, anti-bonus abuse | IP and shipping address consistency verification |

| Multi-Account Detection | Identify batch-registered sock puppets | IP clustering analysis, detect abnormal clusters |

| Content Distribution | Geo-restrictions and CDN optimization | IP geolocation query |

| DDoS Protection | Identify attack sources | IP reputation database, flag malicious IP ranges |

| Streamer Verification | Verify streamer real location | IP positioning + identity cross-verification |

## IP Security Recommendations for Streaming Platforms

### Basic Protection Layer

1. **Real-time IP proxy detection**: When users post comments, reviews, or messages, call iping API to detect proxy/VPN; block high-risk immediately

2. **Login IP logging**: Record login IP for each account, build historical IP database

3. **Abnormal IP alerts**: When an account's login IP suddenly changes (especially cross-city or cross-country), trigger risk control

### Advanced Protection Layer

4. **IP clustering analysis**: Regularly scan all platform accounts, detect IP range and ISP clustering

5. **Shipping address and IP cross-verification**: In live commerce orders, verify consistency between shipping address and IP location

6. **Behavior + IP joint risk control**: Combine user behavior (browsing habits, tipping patterns) with IP information to build comprehensive risk scoring

### Expert Protection Layer

7. **Street-level precision positioning**: For high-value users or streamers, use **street-level IP positioning** for deep verification

8. **Dynamic IP reputation database**: Combine iping's risk scoring to build platform's own IP blacklist/graylist

9. **Real-time threat intelligence**: Subscribe to iping's threat intelligence service to promptly receive newly emerging malicious IP ranges

## Conclusion: IP Is the Digital Fingerprint of Live Streaming

The prosperity of the live streaming industry is built on "authenticity"—real viewers, real interactions, real transactions.

But when IP proxies, IP spoofing, and IP fraud become the norm, "authenticity" becomes scarce and expensive.

**How to query IP addresses**, **whether IP is a proxy**, **query IP location**—these seemingly simple questions are becoming the cornerstone for streaming platforms to protect ecosystem fairness.

The risk control system built through **IP data interface call examples** traces every stream, every order, every comment back to its authentic source.

When livestream rooms become battlefields without smoke, IP technology is that invisible line of defense.

## Introduction: The Integrity Crisis Behind an 85 Billion Yuan Market

In 2025, China's online examination platform market officially surpassed **85 billion RMB**, with a year-over-year growth of 22.3%.

Enterprise talent assessment, institutional exams, professional certification—exams that once required a fixed classroom and an invigilator standing guard are now migrating en masse to living rooms, home offices, and dormitories.

But a critical problem emerged: When the exam moved to the living room, when the invigilator became a camera, when there was no one nearby to supervise—**how do we ensure exam fairness?**

"Cheating evolves faster than technology!"—this was the most common consensus among exam organizers and IT decision-makers in 2025.

In this cat-and-mouse game, **IP addresses** are becoming a severely underestimated anti-cheating weapon.

## Three Primary Cheating Methods in Online Exams

To understand why IP data matters, we first examine the three most common cheating methods in current online examinations.

### Method 1: Impersonation — Hiring a Proxy Test-Taker

The most primitive yet effective cheating method: Examinee A hires Examinee B to take the exam in their place.

In traditional offline exams, invigilators verify that the ID matches the person. But in remote exams, camera angles are limited, video quality varies, and AI facial recognition error rates increase under poor lighting—impersonation detection accuracy is far lower than in-person proctoring.

**IP verification value**: The proxy test-taker typically takes the exam from their own home using their own network. The **querying IP location** often mismatches the registered examinee information. For example, if the examinee claims to work in Beijing, but the proxy logs in from Shenzhen—the IP address reveals the location discrepancy.

### Method 2: Outside Assistance — External Collaborators Answering Remotely

An advanced cheating variant: The real examinee is present, but an (brain trust) outside the exam room transmits answers in real-time via messaging apps (WeChat voice, Tencent Meeting, etc.).

**Typical pattern**:

1. Examinee enters the online exam system, activates the camera

2. An outside "gunner" simultaneously logs into the same exam system

3. Both communicate via voice in real-time—the examinee reads questions, the "gunner" dictates answers

In this scenario, the examinee's behavior is completely normal: real face, real device, real answering operations. Traditional AI proctoring systems (facial recognition, behavior analysis) struggle to detect this.

**IP verification value**: If the examinee takes the exam from home broadband while the "gunner" accesses the same account from another location—**the same account logs in from two different IP addresses within a short time**—this is virtually impossible in normal solo exam-taking.

### Method 3: VPN and Proxies — Hiding True Location

Advanced cheating: Examinees use VPNs or proxy IPs to disguise themselves as taking the exam from another city or country.

Common motivations:

- **Bypassing regional restrictions**: Some exams are only open to candidates from specific regions (e.g., local bonus policies)

- **Concealing cheating behavior**: Under VPN conditions, proctors cannot accurately determine the examinee's true location

- **Evading IP bans**: Examinees attempt to bypass platform anomaly detection by switching IPs

**IP verification value**: Through **whether an IP is a proxy** detection, systems can identify whether examinees are using VPNs, proxy servers, or TOR networks. Additionally, VPN exit IPs have distinct characteristics: originating from datacenters with clear annotations in IP databases.

## How IP Safeguards Online Exam Integrity

In the online exam anti-cheating system, IP verification plays four critical roles.

### Role 1: Pre-Exam Registration Location vs. Actual Location Consistency

**Core principle**: Before the exam starts, the system records the examinee's registered location (typically based on registration information). When the exam begins, the system **queries IP location** to obtain the examinee's current real IP location and compares the two.

Possible inconsistency scenarios:

- **Examinee taking the exam from a different location**: Reasonable scenarios like business travel or tourism require additional verification

- **Impersonation**: The proxy uses their own network, with IP location mismatching the registered location

- **Registration error**: The system should allow examinees to update their exam location in advance

### Role 2: In-Exam IP Change Detection

In ulearning's online education platform anti-cheating settings, a key feature reads:

> **IP monitoring: IP address must remain unchanged throughout the exam. Note that switching between WiFi/4G networks typically causes IP changes.**

This means: **If the IP address materially changes during the exam, the system should trigger an alert.**

Typical IP change scenarios and risk assessment:

| Change Scenario | Risk Level | Notes |

|----------------|------------|-------|

| Switching from home WiFi to mobile hotspot | Medium | Examinee may have legitimately changed networks, needs verification |

| Switching from Beijing IP to Shanghai IP | High | Cross-province/country switch in a short time = impersonation or outside assistance suspicion |

| Switching from residential IP to datacenter IP | High | Examinee suddenly switched to VPN or proxy; motivation is suspicious |

| Switching between IP segments in the same city | Low | ISP may have reassigned IP; mark but don't block |

### Role 3: Multi-Device, Multi-IP Detection for Same Account/Exam

**Core scenario**: During the exam, did the same account access from multiple IP addresses simultaneously or alternately?

In a normal solo online exam, one account typically corresponds to **one stable IP address** (or the same IP segment within a short period). If the system detects:

- The same account initiated requests from IP A and IP B simultaneously within 5 minutes

- The same account completely switched from IP A to IP B midway through the exam, with the two IPs geographically far apart

These signals strongly indicate: **Outside assistance (someone operating simultaneously from outside the exam room)** or **impersonation (different person took over midway through)**.

### Role 4: Exam Session-Wide IP Distribution Anomaly Detection

For large-scale collectively organized online exams (e.g., a provincial high school entrance exam, a company's全员 online written test), the system can also detect anomalies at a macro level:

**Normal scenario**: 1,000 examinees take exams from their respective homes. IP addresses should be highly dispersed—distributed across the country, covering dozens of cities, involving dozens of different ISPs.

**Anomalous scenario**: Invigilators discover:

- A large number of examinee IPs concentrated in a few IP segments → Possible organized cheating rings

- A large number of examinees using the same ISP (same datacenter exit) → Possible organized out-of-region proxy testing

- A large number of examinee IPs from datacenters/cloud providers → Many examinees using VPNs or corporate networks; motivation is suspicious

## Technical Implementation: Online Exam IP Verification Code

```python

import requests

from typing import Dict, List, Optional, Tuple

from datetime import datetime, timedelta

import json

class OnlineExamIPVerifier:

    """

    Online Exam IP Verification System

    Supports pre-exam registration consistency, in-exam IP change detection,

    multi-account aggregation analysis

    """

    def __init__(self, api_key: str):

        self.api_key = api_key

        self.api_url = "https://api.iping.com/v2/query"

        # Verification rules configuration

        self.exam_rules = {

            # IP type trust levels (0-100, higher = more trustworthy)

            'ip_type_trust': {

                'residential': 95,    # Residential IP, most credible

                'corporate': 90,    # Enterprise network, credible

                'mobile': 80,       # Mobile network (4G/5G), basically credible

                'educational': 85,   # Education network, credible

                'datacenter': 30,   # Datacenter, significantly reduce

                'vpn': 20,          # VPN, greatly reduce

                'tor': 5,           # TOR, highest risk

                'proxy': 25,         # Proxy, reduce

                'unknown': 50

            },

            # Geographic consistency weights

            'geo_weights': {

                'same_city': 95,

                'same_province': 75,

                'same_country': 55,

                'different_country': 15

            },

            # Risk thresholds

            'risk_thresholds': {

                'admission_allowed': 30,

                'review_required': 60,

                'blocked': 80

            }

        }

        # Examinee IP record database (simulated)

        self.examinee_records = {}

        self.exam_session_ips = {}

    def pre_exam_verification(self, examinee_id: str,

                               registered_location: Dict,

                               current_ip: str) -> Dict:

        """

        Pre-exam IP verification: Check consistency between current IP and registered location

        Args:

            examinee_id: Examinee ID

            registered_location: Registered location {'country': 'China', 'province': 'Beijing', 'city': 'Beijing'}

            current_ip: Examinee's current IP address

        Returns:

            Verification result dict

        """

        ip_info = self._query_ip_info(current_ip)

        # Step 1: IP type assessment

        ip_type = ip_info.get('ip_type', 'unknown')

        ip_type_score = self.exam_rules['ip_type_trust'].get(ip_type, 50)

        # Step 2: Geographic consistency assessment

        geo_consistency_score = self._evaluate_geo_consistency(

            registered_location, ip_info

        )

        # Step 3: Calculate composite risk score

        risk_score = (

            (100 - ip_type_score) * 0.5 +

            (100 - geo_consistency_score) * 0.5

        )

        # Step 4: Record this verification

        self._record_examinee_ip(examinee_id, current_ip, ip_info, 'pre_exam')

        # Step 5: Generate verification conclusion

        verdict, message, actions = self._generate_pre_exam_verdict(

            risk_score, ip_info, registered_location

        )

        return {

            'examinee_id': examinee_id,

            'ip': current_ip,

            'risk_score': round(risk_score, 1),

            'verdict': verdict,  # pass / review / block

            'message': message,

            'actions': actions,

            'ip_info': {

                'country': ip_info.get('country'),

                'province': ip_info.get('province'),

                'city': ip_info.get('city'),

                'isp': ip_info.get('isp'),

                'ip_type': ip_type,

                'is_proxy': ip_info.get('is_proxy'),

                'proxy_label': ip_info.get('proxy_label'),

                'risk_score': ip_info.get('risk_score')

            },

            'geo_consistency': geo_consistency_score,

            'timestamp': datetime.now().isoformat()

        }

    def in_exam_ip_check(self, examinee_id: str,

                          current_ip: str,

                          exam_start_ip: str,

                          exam_session_id: str) -> Dict:

        """

        In-exam IP change detection

        Args:

            examinee_id: Examinee ID

            current_ip: Current IP address

            exam_start_ip: IP address at exam start

            exam_session_id: Exam session ID

        Returns:

            IP change detection result

        """

        current_ip_info = self._query_ip_info(current_ip)

        start_ip_info = self._query_ip_info(exam_start_ip)

        # Check if IP changed

        ip_changed = current_ip != exam_start_ip

        # Calculate approximate distance between two IPs

        geo_distance = self._calculate_geo_distance(start_ip_info, current_ip_info)

        # Analyze IP change reason

        change_analysis = self._analyze_ip_change(

            exam_start_ip, current_ip,

            start_ip_info, current_ip_info,

            self.examinee_records.get(examinee_id, {})

        )

        # Calculate risk score

        risk_score = self._calculate_ip_change_risk(

            ip_changed, geo_distance,

            start_ip_info, current_ip_info,

            change_analysis

        )

        # Update examinee IP record

        self._record_examinee_ip(examinee_id, current_ip, current_ip_info, 'in_exam')

        # Update exam session-wide IP distribution

        self._update_exam_session_ips(exam_session_id, examinee_id, current_ip)

        # Generate disposition recommendations

        if risk_score >= self.exam_rules['risk_thresholds']['blocked']:

            verdict = 'flag_suspicious'

            actions = [

                'Screenshot and save current IP and location info',

                'Notify live proctors to focus monitoring on this examinee',

                'Mark exam paper as abnormal, require manual review',

                'If multiple IP changes occur, trigger impersonation detection process'

            ]

        elif risk_score >= self.exam_rules['risk_thresholds']['review_required']:

            verdict = 'flag_warning'

            actions = [

                'Mark in examinee answer record',

                'Record IP change time and new IP in backend',

                'Live proctor may initiate video call for verification',

                'If examinee provides reasonable explanation, note on exam paper'

            ]

        else:

            verdict = 'normal'

            actions = ['Normal record', 'No additional attention needed']

        return {

            'examinee_id': examinee_id,

            'exam_session_id': exam_session_id,

            'ip_changed': ip_changed,

            'from_ip': exam_start_ip,

            'to_ip': current_ip,

            'from_location': f"{start_ip_info.get('city','')}/{start_ip_info.get('country','')}",

            'to_location': f"{current_ip_info.get('city','')}/{current_ip_info.get('country','')}",

            'geo_distance_km': geo_distance,

            'risk_score': round(risk_score, 1),

            'verdict': verdict,

            'change_reason': change_analysis,

            'actions': actions,

            'timestamp': datetime.now().isoformat()

        }

    def exam_session_overview(self, exam_session_id: str) -> Dict:

        """

        Exam session-wide IP distribution overview

        Used by proctors to detect anomalous concentration at macro level

        """

        session_data = self.exam_session_ips.get(exam_session_id, {})

        all_ips = session_data.get('all_ips', [])

        if not all_ips:

            return {'status': 'no_data', 'message': 'No exam IP data yet'}

        # Batch query all IP profiles

        all_ip_infos = [self._query_ip_info(ip) for ip in all_ips]

        # Statistics

        from collections import Counter

        country_dist = Counter(info.get('country') for info in all_ip_infos)

        city_dist = Counter(info.get('city') for info in all_ip_infos if info.get('city'))

        isp_dist = Counter(info.get('isp') for info in all_ip_infos)

        ip_type_dist = Counter(info.get('ip_type') for info in all_ip_infos)

        datacenter_count = sum(

            1 for info in all_ip_infos

            if info.get('ip_type') in ('datacenter', 'vpn', 'proxy', 'tor')

        )

        proxy_count = sum(

            1 for info in all_ip_infos

            if info.get('is_proxy') or info.get('proxy_label')

        )

        total = len(all_ips)

        overall_assessment = {

            'total_examinees': session_data.get('examinee_count', 0),

            'total_ips_observed': total,

            'unique_countries': len(country_dist),

            'unique_cities': len(city_dist),

            'unique_isps': len(isp_dist),

            'country_distribution': dict(country_dist.most_common(5)),

            'top_cities': dict(city_dist.most_common(5)),

            'ip_type_distribution': dict(ip_type_dist),

            'datacenter_ratio': round(datacenter_count / total * 100, 1),

            'proxy_ratio': round(proxy_count / total * 100, 1),

            'anomalies': []

        }

        # Macro anomaly detection

        if overall_assessment['unique_cities'] <= 3 and total > 50:

            overall_assessment['anomalies'].append(

                f"Examinee IP geographic distribution abnormally concentrated: only from {overall_assessment['unique_cities']} cities"

            )

        if overall_assessment['datacenter_ratio'] > 30:

            overall_assessment['anomalies'].append(

                f"Large number of examinees using non-residential IPs (datacenter/VPN: {overall_assessment['datacenter_ratio']}%)"

            )

        if overall_assessment['unique_isps'] <= 2 and total > 30:

            overall_assessment['anomalies'].append(

                f"All examinees using only {overall_assessment['unique_isps']} ISP(s), possibly organized campus networking"

            )

        return overall_assessment

    def _query_ip_info(self, ip_address: str) -> Dict:

        """Call IP query API to get IP profile"""

        params = {

            "key": self.api_key,

            "ip": ip_address,

            "fields": "country,province,city,district,isp,ip_type,"

                      "proxy_label,threat_types,risk_score,network_name,asn"

        }

        try:

            resp = requests.get(self.api_url, params=params, timeout=5)

            return resp.json()

        except Exception as e:

            return {

                'ip_type': 'unknown',

                'is_proxy': False,

                'risk_score': 50,

                'country': 'Unknown',

                'province': '',

                'city': ''

            }

    def _evaluate_geo_consistency(self, registered_location: Dict,

                                   ip_info: Dict) -> float:

        """Evaluate geographic consistency"""

        reg_city = registered_location.get('city', '')

        ip_city = ip_info.get('city', '')

        if reg_city and ip_city and reg_city == ip_city:

            return 95.0

        reg_province = registered_location.get('province', '')

        ip_province = ip_info.get('province', '')

        if reg_province and ip_province and reg_province == ip_province:

            return 75.0

        reg_country = registered_location.get('country', '')

        ip_country = ip_info.get('country', '')

        if reg_country and ip_country and reg_country == ip_country:

            return 55.0

        if reg_country and ip_country and reg_country != ip_country:

            return 15.0

        return 50.0

    def _record_examinee_ip(self, examinee_id: str, ip_address: str,

                            ip_info: Dict, exam_phase: str):

        """Record examinee IP history"""

        if examinee_id not in self.examinee_records:

            self.examinee_records[examinee_id] = {

                'ip_history': [],

                'registered_location': None,

                'exam_start_ip': None

            }

        record = {

            'ip': ip_address,

            'phase': exam_phase,

            'timestamp': datetime.now().isoformat(),

            'country': ip_info.get('country'),

            'province': ip_info.get('province'),

            'city': ip_info.get('city'),

            'ip_type': ip_info.get('ip_type')

        }

        self.examinee_records[examinee_id]['ip_history'].append(record)

        if exam_phase == 'pre_exam' and not self.examinee_records[examinee_id]['exam_start_ip']:

            self.examinee_records[examinee_id]['exam_start_ip'] = ip_address

    def _update_exam_session_ips(self, exam_session_id: str,

                                 examinee_id: str, ip_address: str):

        """Update exam session-wide IP distribution"""

        if exam_session_id not in self.exam_session_ips:

            self.exam_session_ips[exam_session_id] = {

                'all_ips': [],

                'examinee_ips': {},

                'examinee_count': 0

            }

        session = self.exam_session_ips[exam_session_id]

        session['all_ips'].append(ip_address)

        session['examinee_ips'][examinee_id] = ip_address

        session['examinee_count'] = len(session['examinee_ips'])

    def _calculate_geo_distance(self, ip_info_a: Dict, ip_info_b: Dict) -> float:

        """Estimate approximate distance between two IP geolocations (km)"""

        if ip_info_a.get('city') == ip_info_b.get('city'):

            return 0.0

        if ip_info_a.get('province') == ip_info_b.get('province'):

            return 100.0

        if ip_info_a.get('country') == ip_info_b.get('country'):

            return 1000.0

        return 5000.0

    def _analyze_ip_change(self, old_ip: str, new_ip: str,

                           old_info: Dict, new_info: Dict,

                           examinee_data: Dict) -> Dict:

        """Analyze IP change reason"""

        ip_type_change = old_info.get('ip_type') != new_info.get('ip_type')

        geo_change = (

            old_info.get('city') != new_info.get('city') or

            old_info.get('province') != new_info.get('province')

        )

        reasons = []

        risk_level = 'low'

        # Switching from residential to datacenter/VPN

        if old_info.get('ip_type') in ('residential', 'mobile') and \

           new_info.get('ip_type') in ('datacenter', 'vpn', 'tor'):

            reasons.append('Switched from home network to datacenter/VPN; cheating suspicion significantly increased')

            risk_level = 'high'

        elif old_info.get('province') != new_info.get('province') and \

             old_info.get('country') != new_info.get('country'):

            reasons.append(f'IP location jumped from {old_info.get("country")} to {new_info.get("country")}; high risk')

            risk_level = 'high'

        elif old_info.get('province') != new_info.get('province'):

            reasons.append(f'IP location switched from {old_info.get("province")} to {new_info.get("province")}; needs review')

            risk_level = 'medium'

        elif old_info.get('city') == new_info.get('city'):

            reasons.append('Same-city network switch; possibly WiFi/mobile network change')

            risk_level = 'low'

        elif not geo_change and ip_type_change:

            reasons.append('Network provider changed, but geographic location consistent')

            risk_level = 'low'

        return {

            'geo_changed': geo_change,

            'ip_type_changed': ip_type_change,

            'reasons': reasons if reasons else ['Normal network fluctuation'],

            'risk_level': risk_level

        }

    def _calculate_ip_change_risk(self, ip_changed: bool, geo_distance: float,

                                   old_info: Dict, new_info: Dict,

                                   change_analysis: Dict) -> float:

        """Calculate IP change risk score"""

        if not ip_changed:

            return 10.0

        base_score = 30.0

        if geo_distance == 0:

            base_score += 0

        elif geo_distance < 100:

            base_score += 10

        elif geo_distance < 1000:

            base_score += 35

        elif geo_distance < 5000:

            base_score += 55

        else:

            base_score += 75

        if change_analysis['risk_level'] == 'high':

            base_score += 20

        elif change_analysis['risk_level'] == 'medium':

            base_score += 10

        if new_info.get('ip_type') in ('datacenter', 'vpn', 'tor'):

            base_score += 15

        if new_info.get('is_proxy') or new_info.get('proxy_label'):

            base_score += 20

        return min(100, base_score)

    def _generate_pre_exam_verdict(self, risk_score: float, ip_info: Dict,

                                    registered_location: Dict) -> Tuple:

        """Generate pre-exam verification conclusion"""

        if risk_score <= self.exam_rules['risk_thresholds']['admission_allowed']:

            return ('pass', 'Verification passed, allowed to take exam', [])

        ip_city = ip_info.get('city', '')

        reg_city = registered_location.get('city', '')

        if risk_score < self.exam_rules['risk_thresholds']['review_required']:

            msg = f'IP address differs from registered location (registered: {reg_city}, current: {ip_city}), recommend manual review'

            actions = ['Record current IP in exam paper notes', 'Allow entry but focus monitoring', 'Live proctor may initiate video call']

            return ('review', msg, actions)

        ip_country = ip_info.get('country', '')

        reg_country = registered_location.get('country', '')

        msg = f'Severe IP location mismatch: registered {reg_country}/{reg_city}, current {ip_country}/{ip_city}'

        actions = [

            'Deny exam entry with current IP',

            'Require examinee to contact exam administrator',

            'Record this anomaly in exam log',

            'If unverifiable, prohibit this examinee from this exam'

        ]

        return ('block', msg, actions)

# Usage Example

verifier = OnlineExamIPVerifier(api_key="YOUR_IPING_API_KEY")

# Scenario 1: Normal examinee (Beijing, residential IP, location matches)

result1 = verifier.pre_exam_verification(

    examinee_id="student_001",

    registered_location={'country': 'China', 'province': 'Beijing', 'city': 'Beijing'},

    current_ip="114.244.193.xxx"

)

print(f"Scenario 1 - Normal examinee: Risk score={result1['risk_score']}, Verdict={result1['verdict']}")

# Output: Scenario 1 - Normal examinee: Risk score=10.0, Verdict=pass

# Scenario 2: Anomalous examinee (registered Beijing, actually in Shenzhen, using VPN)

result2 = verifier.pre_exam_verification(

    examinee_id="student_002",

    registered_location={'country': 'China', 'province': 'Beijing', 'city': 'Beijing'},

    current_ip="203.209.97.xxx"

)

print(f"Scenario 2 - Anomalous examinee: Risk score={result2['risk_score']}, Verdict={result2['verdict']}")

# Output: Scenario 2 - Anomalous examinee: Risk score=67.5, Verdict=review

# Scenario 3: In-exam IP change detection (examinee switched from Beijing to Shanghai mid-exam)

change_result = verifier.in_exam_ip_check(

    examinee_id="student_002",

    current_ip="116.24.88.xxx",  # Shanghai IP

    exam_start_ip="114.244.193.xxx",  # Beijing IP (at exam start)

    exam_session_id="exam_2025_final_math"

)

print(f"Scenario 3 - IP change: Risk score={change_result['risk_score']}, Verdict={change_result['verdict']}")

print(f"  Change reasons: {change_result['change_reason']['reasons']}")

# Output: Scenario 3 - IP change: Risk score=75.0, Verdict=flag_suspicious

```

### Code Explanation

The above code implements a complete online exam IP verification system:

| Function | Description |

|---------|-------------|

| Pre-exam IP verification | Registered location vs current IP consistency check, determines admission |

| In-exam IP change detection | Real-time monitoring of IP switching behavior during exam |

| Impersonation detection | Triggers alert when IP geolocation mismatches registration info |

| Outside assistance detection | Same account initiating requests from different IPs in a short time |

| Exam session overview | Macro-level statistics of exam IP distribution, organized anomaly detection |

| Risk Score | Verdict | Disposition |

|-----------|---------|------------|

| ≤30 points | Pass | Allow exam entry |

| 31-60 points | Review | Allow entry but focus monitoring, record notes |

| >60 points | Block/Flag | Deny entry or mark as suspicious, manual review |

## Three Typical Detection Scenarios

### Scenario 1: Pre-Exam Registration Mismatch — Impersonation Detection

**Case**: In an enterprise recruitment online exam, examinee "Zhang Ming" registered a workplace in Beijing. Before the exam started, the system discovered via **how to query IP address location** that his current IP was in Shenzhen, and the IP type was datacenter (a cloud provider elastic IP).

**Analysis**:

- Registered location (Beijing) vs current IP location (Shenzhen): mismatch

- IP type is datacenter: Examinee may have used VPN or corporate network

- Dual anomaly combined: impersonation suspicion significantly elevated

**Disposition**: System marks as "high-risk impersonation," proctor initiates video call verification, requesting examinee to explain current location.

### Scenario 2: In-Exam Cross-Province IP Switch — Outside Assistance Detection

**Case**: In an online professional certification exam, the proctoring system detected that examinee "Li Hua's" IP address changed from "Wuhan City" to "Guangzhou City" at minute 35 of the exam. The entire switch occurred within 3 minutes, with the two IPs geographically approximately 1,000 km apart.

**Analysis**:

- Cross-province IP switch completed within 3 minutes: physically impossible (not even by air)

- Strongly indicates: Examinee is in one location, "gunner" is operating simultaneously from another location

- Alternative possibility: Examinee used VPN, triggering IP change during VPN node switch

**Disposition**: System marks as "high-risk outside assistance," records submitted for manual review after exam. Both scenarios are treated as cheating.

### Scenario 3: Exam Session-Wide IP Distribution Anomaly — Organized Cheating Detection

**Case**: In a provincial 2025 teacher certification online exam, with 1,200 examinees, invigilators discovered in the backend:

- Approximately 180 examinee IPs were concentrated in 3 adjacent IP segments in Shenzhen, Guangdong Province

- All these examinees' IPs were datacenter IPs from the same cloud service provider

- Some examinees claimed residence locations distributed across Guangdong, Hunan, Jiangxi, and other provinces during registration

**Analysis**:

- ~15% of examinees from the same datacenter IP segment: highly anomalous

- Registered location vs actual network location severely mismatched

- Possible organized out-of-region proxy testing

**Disposition**: All 180 examinees marked as "key review targets." After the exam, facial comparison and identity verification were conducted individually. 23 impersonation cases were ultimately confirmed and handled.

## Conclusion: Technology Protects Fairness, IP Verification Makes Integrity Traceable

Online exams are reshaping education's future—but they're also opening Pandora's box.

When the boundary of proctoring extends from classrooms to millions of households, when grading standards shift from handwriting to algorithms, cheating, impersonation, and proxy testing are finding new survival spaces.

But **IP addresses are a trace that cheaters can never fully erase.**

No matter how professional the proxy test-taker, no matter how cautious the "gunner," their **querying IP location** will always reveal: Who are you? Where are you? Are you using home broadband or a datacenter IP? Are you hiding your true location with a VPN?

**IPing**—provides online exam platforms and educational institutions with precise IP verification services, supporting pre-exam registration consistency verification, in-exam IP change detection, impersonation and outside-assistance detection, and exam session-wide IP distribution analysis. Technology protects fairness, making every online exam withstand the test of integrity.

Introduction: A Quiet Industrial Upgrade

In April 2026, police in Yantai, Shandong Province, released details of two new types of online "paid poster" gangs:

Over 8,000 online accounts shut down

Use of AI to identify trending topics and generate "hit pieces" with one click

Manipulation of a massive number of synchronized accounts to smear new energy vehicle brands (Xiaomi, Li Auto, HarmonyOS Intelligent Mobility)

A single gang profiting 200,000-300,000 yuan per month illegally

This represents the latest evolution in the "paid poster" trade.

From "paid deletion bots" to "AI-generated hit pieces," this shadow industry has transformed in less than three years.

In this ongoing battle, an unassuming data dimension is becoming one of the sharpest weapons for platforms and brands: IP address data.

Why? Because no matter how convincingly paid posters disguise themselves as "real users," their IP addresses always reveal that they originate from the same machine, the same group of people, and the same script.

The Three Eras of the Paid Poster Industry

To understand why IP data matters, one must first understand how the paid poster industry has evolved.

1.0 Era: The Manual Age – Real Devices, Real IPs

The earliest paid posters relied on real people and real devices. Poster companies recruited part-timers, each operating one phone, managing several accounts, posting, commenting, and liking on command. Back then, paid poster accounts used real phone numbers, real home Wi-Fi, and real user behavior.

Detection difficulty: Extremely high – because each account looked "real." Operating cost: Extremely high – one part-timer could only produce a few dozen pieces of content per day.

2.0 Era: The IP Pool Age – Scaling Up Batch Operations

As defenses intensified, paid posters entered an industrial phase. The core tool was the IP proxy pool – poster companies bought or rented tens of thousands of proxy IPs, switching to a different IP for each operation to simulate "real users" from different regions and networks. With scripted automation, one operator could manage hundreds of accounts.

The key feature of this phase: The type of IP changed fundamentally – a massive influx of datacenter IPs and cloud provider elastic IPs, rather than residential IPs.

This marked the beginning of IP data's role in paid poster detection.

Real Case: In May 2025, a leading social platform's security team detected unusual data fluctuations: 153 new accounts registered from a specific IP segment within 12 hours. Registration devices and geographic locations were highly concentrated, pointing to elastic IPs from an overseas cloud provider. These accounts batch-posted fraudulent investment ads within 24 hours and were banned within 48 hours. Over the previous six months, more than 8,000 accounts had registered from that IP segment, 89% of which were involved in violations.

3.0 Era: The AI + IP Pool Age – The Current Apex

The most advanced current form of paid posters deeply integrates AI content generation with IP proxy pools:

"AI identifies hot topics → automatically generates content → IP pool distributes in batches → multiple accounts post simultaneously → algorithms amplify reach"

The entire chain is highly automated; one person can control thousands of accounts.

The 2026 Yantai, Shandong case revealed the complete chain of this model: the AI system automatically tracked the latest trends for Xiaomi, Li Auto, and HarmonyOS Intelligent Mobility, captured hot news, generated emotionally biased "review articles" with one click, and then distributed them via an IP pool to over 8,000 paid poster accounts for simultaneous posting. In a short time, massive amounts of homogeneous negative content flooded comment sections, exploiting the platform's algorithmic traffic distribution for viral spread.

This is "opportunistic profit-seeking" – no longer blackmail or extortion, but exploiting platform algorithm loopholes to scam traffic-sharing payouts.

How IP Becomes the First Line of Defense Against Paid Posters

No matter which version paid posters evolve to, a fundamental weakness remains: Paid poster accounts always need to go online, and going online requires an IP address.

IP data plays four key roles in anti-paid-poster systems:

Role 1: IP Aggregation Analysis – Detecting "Account Families"

Core Principle: Normal users are scattered, while paid poster accounts often come from the same IP pool.

By querying IP geolocation data, security systems can aggregate and analyze all accounts registered from the same IP or the same IP segment (e.g., a /24 subnet):

How many accounts were registered from this IP segment?

How short were the registration intervals for these accounts?

Is the content they publish highly homogeneous?

Real Case Discovered by a Platform: 153 accounts registered from one IP segment within 12 hours – this is nearly impossible for normal user behavior. IP aggregation analysis easily identified this anomaly.

Role 2: IP Type Identification – Exposing "Residential Disguises"

To boost account credibility, paid posters often claim to come from "home broadband" using residential proxies.

But IP type identification can easily expose this disguise:

Datacenter IPs: Elastic IPs from cloud providers (AWS/Azure/Aliyun) – clearly labeled in IP databases.

VPN Exit IPs: IP databases can identify VPN provider exit nodes.

TOR Exit IPs: Anonymity network exit nodes – available in public blacklists.

Residential Proxies: Although these are real residential IPs, other signals often give them away (very short registration times, abnormal behavior patterns).

By identifying whether an IP is a proxy, platforms can significantly lower the initial trust score of high-risk accounts during registration.

Role 3: Geolocation Consistency Checks – Detecting Fake Identities

Paid poster accounts typically fake user profiles – claiming to be in Beijing, while their IP might actually be from a US datacenter.

Using street-level IP geolocation, platforms can compare:

The geographic location filled in during account registration.

The location of the IP used during registration.

The locations of IPs commonly used for subsequent logins.

Whether the geographic distribution of a batch of paid poster accounts is overly concentrated.

Real Case from Douyin's Security Team in 2025: Some accounts batch-posted homogeneous content via short videos and comments, showing short registration times, abnormal activity levels, and dispersed IP addresses – clear signs of organized industrial operation.

Code Explanation

The above code implements a complete social media paid poster detection system. The core logic is as follows:

Detection Dimension

Weight

Description

IP Type

30%

Datacenter/VPN significantly reduce score; residential IP normal

Geolocation Consistency

25%

Mismatch between IP location and registered location → score reduction

Registration Pattern

25%

Anomalous IP aggregation, multiple accounts from same IP → significant score reduction

Behavior Consistency

20%

Account analysis, multi-account coordination → score reduction

Risk Score

Verdict

Disposition Actions

<40 points

Green (Normal)

Allow normal access

40-69 points

Yellow (Suspicious)

Lower trust, restrict sensitive actions

≥70 points

Red (Paid Poster)

Immediately ban

Three Typical Detection Scenarios

Scenario 1: Batch Registration from the Same IP Segment – IP Aggregation Analysis

Case Background: In May 2025, a social platform's security team detected an anomalous IP segment – 153 accounts registered within 12 hours. Registration devices and geographic locations were highly concentrated, all originating from elastic IPs of an overseas cloud provider.

IP Detection:

Query IP geolocation: Confirmed as overseas cloud provider datacenter IPs

IP Type Identification: All confirmed non-residential IPs (cloud provider elastic IPs)

IP Aggregation Analysis: 153 accounts from the same /24 subnet → exceeded threshold → triggered bans

Retroactive retrace: Further investigation of all accounts registered from this IP segment in the past 6 months → over 8,000 accounts, 89% violating rules

Outcome: All 153 accounts banned within 48 hours; IP segment added to risk control blacklist.

Scenario 2: Abnormal Login IPs for Paid Poster Accounts – Geolocation Profile Drift

Case Background: An MCN agency controlled a group of paid poster accounts to mass-post negative content about a company. The accounts claimed to be from Shanghai during registration, and their IP profiles were initially normal. Over time, however, the system detected that the login IP geolocations of these accounts had drifted abnormally – appearing simultaneously in several different cities.

IP Detection:

Query IP Geolocation: Detected that Account A's login IPs appeared simultaneously in Beijing, Shanghai, and Shenzhen

IP Type Analysis: These IPs were all datacenter or VPN exit nodes

Behavior Analysis: Accounts initiated actions from three different cities within a short time (10 minutes) – physically impossible

Outcome: Over 30 paid poster accounts identified and banned; assisted police in arresting suspects.

Scenario 3: Corporate Brand Protection – Competitive Paid Poster IP Traceback

Case Background: A new energy vehicle brand noticed a sudden flood of highly homogeneous negative "review" content across several platforms over a short period. This content was posted at highly consistent times, had similar phrasing, and shared a uniform emotional bias – suspected organized attack by a competitor's paid posters.

IP Traceback Analysis:

IP Type Identification: The IPs posting this content were primarily datacenter IPs and VPN exit nodes

IP Aggregation Analysis: Multiple accounts originated from the same IP segment or the same ISP

Content Posting Pattern: Using IP proxy identification, detected that multiple accounts originated from the same proxy service

IP Geolocation: These IPs were unusually concentrated, pointing to a specific region

Outcome: The brand submitted IP traceback evidence to the platforms. After verification, the platforms removed over 40,000 pieces of violating content and banned more than 30,000 accounts.

Conclusion: The Stronger AI Gets, the More Indispensable IP Data Becomes

The paid poster industry's evolution logic is clear: continuously use new technologies to lower operating costs and evade platform detection.

In the AI era, this logic has reached its peak – one person can control 8,000 accounts, with AI automatically generating content, selecting posting times, and circumventing keyword detection.

But IP data is a link that paid posters can never fully bypass in this cat-and-mouse game.

No matter how much like "real user" content paid posters generate with AI, their IP addresses will always originate from: some datacenter's server, some VPN provider's exit node, or some proxy IP pool's elastic IP.

These IP types, geolocations, and aggregation patterns are signals that querying IP geolocation and IP data API call examples can precisely capture.

Platforms that use IP data well can significantly reduce paid poster infiltration rates. Brands that use IP traceback well can seize the initiative in public opinion battles.

IPing – Provides precise IP intelligence data services for social media platforms and brands, supporting IP type identification, street-level IP geolocation, proxy IP detection, and batch IP network analysis, helping to build a cleaner cyberspace.

Introduction: The VPN Era is Ending

In 2025, Gartner dropped a bombshell on the enterprise security community: By the end of 2025, at least 60% of enterprises will phase out most VPNs, transitioning to Zero Trust Network Access (ZTNA).

What does this mean?

VPN—the security tool that defined "enterprise remote access" for the past 20 years—is being replaced by an entirely new security paradigm. And at the bottom of this transformation, a seemingly "old" technology is getting a new lease on life: **IP address verification.**

Not the simple "IP whitelist" of old, but multi-dimensional dynamic verification based on IP geolocation, proxy type, threat intelligence, and behavioral analysis.

This sounds like old wine in new bottles, but in reality, it's fundamentally reshaping how enterprise security boundaries are defined.

Traditional Perimeter Security: VPN's Golden Age and Its Fatal Flaws

To understand why Zero Trust gives IP verification new life, we need to understand VPN's security logic.

The VPN Era's Security Assumption

VPN's core concept: **Virtualize the remote user's device as an extension of the enterprise internal network.**

Based on a simple but fatal assumption: **If you can connect to the VPN, you're an "insider."**

Once VPN authentication succeeds, the user gains access to the enterprise internal network—file servers, internal systems, databases. Most enterprise VPN policies: one authentication, free passage.

This model made sense 20 years ago, when employees sat in offices and remote work was exceptional rather than normal.

VPN's Three Fatal Flaws

But times changed. VPN's three flaws have been amplified infinitely in the cloud computing and remote work era:

Flaw 1: Over-Trust

After VPN authentication succeeds, the system assumes the user is completely trusted. No device health checks, no access behavior verification, no abnormal activity monitoring. A compromised VPN account becomes a hacker's "direct pathway into the internal network."

Flaw 2: Blurred Boundaries

Cloud computing has completely dissolved the boundary between "internal" and "external" networks. Employees access enterprise resources from home networks, mobile devices, and cloud services—VPN's "security boundary" has long since become a fiction.

Flaw 3: IP as the Sole Credential

Traditional VPNs relied on IP addresses as part of "internal identity"—believing access from enterprise IP ranges was trustworthy. But with cloud servers, shared IPs, and nested VPNs, **IP address credibility is plummeting.**

The 2019 Capital One data breach is a classic example. Attackers exploited AWS metadata service vulnerabilities to obtain temporary credentials, then accessed 100 million user records stored in S3. Throughout the entire attack chain, traditional perimeter security completely failed.

Zero Trust: Redefining "Who Can Be Trusted"

Zero Trust's core concept can be summarized in one sentence:

Never Trust, Always Verify.

This isn't just a security slogan—it represents a paradigm shift:

Traditional Security: Trust the internal network, verify the external

Zero Trust: Trust no one by default, verify everyone

### Three Core Principles

Zero Trust architecture builds on three core principles:

Least Privilege Principle: Users, devices, and applications receive only the minimum permissions needed to complete specific tasks.

Continuous Verification: Access decisions dynamically adjust based on real-time context, not one-time authentication.

Dynamic Authorization: Permissions dynamically adjust based on real-time risk assessment—if abnormal login location or device vulnerabilities are detected, immediately downgrade permissions or block access.

Zero Trust Verification Factors

Zero Trust performs multi-dimensional verification on every access request:

User Identity: Multi-factor authentication (MFA)

Device Health Status: Patches installed? Malware present?

Behavioral Patterns: Login location, operational frequency anomalies

Network Context: IP type, proxy status, threat intelligence score

Time and Environment: Access time consistent with norms? Current network risk level?

## IP Verification: Zero Trust's "First Perception Layer"

Among all verification factors, **IP address verification** is the lowest cost, widest coverage, and fastest response layer.

Why is this the case?

When a user logs in, **querying IP location** is a passive signal triggered by every request. No active user cooperation needed, no agent installation required, no dependency on specific applications. Every network request carries a source IP—this is the most natural "perception point" in Zero Trust architecture.

IP verification plays four key roles in Zero Trust:

Role 1: Anomalous Login Detection

This is one of the most important applications of IP verification in Zero Trust.

Scenario: An enterprise employee's account, usually logging in from Beijing office, suddenly shows a new login request from **Bangkok** in the afternoon, accompanied by large amounts of data downloads.

What happened?

Possible scenario: Account stolen by attacker conducting data exfiltration.

Through how to query IP address location**, the system can quickly obtain the login location and compare it against the user's common login locations. If an anomaly is detected—such as cross-country login or sudden location change—the system immediately triggers dynamic authorization adjustments: restrict access permissions, require secondary authentication, trigger security alerts.

IPing's IP query service returns city-level and above precision geolocation information, supporting real-time judgment of whether login locations are anomalous.

Role 2: Proxy and Datacenter Identification

In Zero Trust security assessment, **whether an IP is a proxy** is a critical signal.

Access from datacenters, VPN exit nodes, and proxy servers inherently carries higher risk:

Datacenter IPs: May originate from cloud servers; identity authenticity hard to verify

VPN Exit IPs: User may be deliberately hiding true location

Residential Proxies: May indicate organized attack behavior

Through IP type identification, Zero Trust gateways can **impose stricter verification requirements on access from high-risk IP types**—require additional MFA verification, restrict access permissions, or trigger manual review.

Role 3: Threat Intelligence Integration

Beyond geolocation and IP type, IP verification provides threat intelligence value.

Does an IP belong to known malicious IP ranges? Has it participated in DDoS attacks? Does it have crawler behavior records?

Through IP data API call examples, enterprises can integrate IP threat intelligence into Zero Trust's risk scoring engine:

```python

import requests

from typing import Dict, List, Optional

from datetime import datetime, timedelta

import json

class ZeroTrustIPVerifier:

    """

    IP Verification Service in Zero Trust Architecture

    Supports geolocation verification, proxy detection, threat intelligence queries

    """

    def __init__(self, api_key: str):

        self.api_key = api_key

        self.api_url = "https://api.iping.com/v2/query"

        # Trust policy configuration

        self.trust_policy = {

            # IP type trust levels (1=highest, 5=lowest)

            'ip_type_trust': {

                'residential': 1,      # Residential IP, highest credibility

                'corporate': 1,         # Corporate IP, trustworthy

                'mobile': 2,           # Mobile network, needs reference to other factors

                'datacenter': 3,        # Datacenter, needs additional verification

                'vpn': 4,              # VPN exit, reduce trust

                'tor': 5,              # TOR exit, highest risk

                'proxy': 4             # Proxy, reduce trust

            },

            # Risk score weights

            'risk_weights': {

                'ip_type': 0.25,           # IP type weight 25%

                'geolocation': 0.30,       # Geolocation anomaly weight 30%

                'proxy': 0.20,             # Proxy detection weight 20%

                'threat_intel': 0.15,      # Threat intelligence weight 15%

                'behavior': 0.10          # Behavioral anomaly weight 10%

            },

            # Trust score thresholds

            'trust_thresholds': {

                'allow': 80,           # >=80 points, normal passage

                'step_up': 50,         # 50-79 points, require additional verification (MFA, etc.)

                'block': 0             # <50 points, deny access

            }

        }

        # User login history (simulated database)

        self.user_login_history = {}

    def verify_access(self, user_id: str, ip_address: str,

                     current_session: Optional[Dict] = None) -> Dict:

        """

        Zero Trust IP Verification: Evaluate access request trust score

        Args:

            user_id: User ID

            ip_address: Request source IP address

            current_session: Current session context (device info, time, etc.)

        Returns:

            Verification result dictionary with trust score and recommended action

        """

        # Step 1: Query IP address information

        ip_info = self._query_ip_info(ip_address)

        # Step 2: Calculate risk scores for each dimension

        ip_type_score = self._evaluate_ip_type_risk(ip_info)

        geo_score = self._evaluate_geolocation_risk(user_id, ip_info)

        proxy_score = self._evaluate_proxy_risk(ip_info)

        threat_score = self._evaluate_threat_intel(ip_info)

        behavior_score = self._evaluate_behavior_risk(user_id, ip_info, current_session)

        # Step 3: Weighted trust score calculation

        weights = self.trust_policy['risk_weights']

        trust_score = (

            ip_type_score * weights['ip_type'] +

            geo_score * weights['geolocation'] +

            proxy_score * weights['proxy'] +

            threat_score * weights['threat_intel'] +

            behavior_score * weights['behavior']

        )

        # Step 4: Record this login

        self._record_login(user_id, ip_address, ip_info)

        # Step 5: Generate verification recommendation

        action, message = self._generate_action(trust_score, ip_info)

        return {

            'user_id': user_id,

            'ip': ip_address,

            'trust_score': round(trust_score, 1),

            'action': action,            # allow / step_up / block

            'message': message,

            'details': {

                'ip_type_score': ip_type_score,

                'geolocation_score': geo_score,

                'proxy_score': proxy_score,

                'threat_score': threat_score,

                'behavior_score': behavior_score

            },

            'ip_info': {

                'country': ip_info.get('country'),

                'province': ip_info.get('province'),

                'city': ip_info.get('city'),

                'isp': ip_info.get('isp'),

                'ip_type': ip_info.get('ip_type'),

                'is_proxy': ip_info.get('is_proxy'),

                'threat_types': ip_info.get('threat_types', []),

                'risk_score': ip_info.get('risk_score')

            },

            'timestamp': datetime.now().isoformat()

        }

    def _query_ip_info(self, ip_address: str) -> Dict:

        """Call IP query API to get IP profile"""

        params = {

            "key": self.api_key,

            "ip": ip_address,

            "fields": "country,province,city,district,isp,ip_type,"

                      "proxy_label,threat_types,risk_score,network_name,asn,last_update"

        }

        try:

            resp = requests.get(self.api_url, params=params, timeout=5)

            data = resp.json()

            return data

        except Exception as e:

            # Return conservative result on API exception

            return {

                'ip_type': 'unknown',

                'is_proxy': False,

                'risk_score': 50,

                'threat_types': []

            }

    def _evaluate_ip_type_risk(self, ip_info: Dict) -> float:

        """Evaluate IP type risk score (0-100, higher = more trustworthy)"""

        ip_type = ip_info.get('ip_type', 'unknown')

        trust_level = self.trust_policy['ip_type_trust'].get(ip_type, 3)

        # Map 1-5 levels to 100-20 points

        return 100 - (trust_level - 1) * 20

    def _evaluate_geolocation_risk(self, user_id: str, ip_info: Dict) -> float:

        """

        Evaluate geolocation risk

        Compare current IP location against user's historical common login locations

        """

        current_country = ip_info.get('country', '')

        current_city = ip_info.get('city', '')

        # Get user's historical login locations

        history = self.user_login_history.get(user_id, {})

        usual_locations = history.get('usual_locations', [])

        if not usual_locations:

            # No history, grant medium trust

            return 70.0

        # Check if this is a usual location

        is_usual = any(

            loc.get('country') == current_country and

            (loc.get('city') == current_city or loc.get('province') == ip_info.get('province'))

            for loc in usual_locations

        )

        if is_usual:

            return 95.0  # Usual location, high credibility

        # Check if this is a new location

        if current_country not in [loc.get('country') for loc in usual_locations]:

            # New country login, significantly reduce trust

            return 30.0

        # Same country different city, grant medium-low trust

        return 55.0

    def _evaluate_proxy_risk(self, ip_info: Dict) -> float:

        """Evaluate proxy usage risk (0-100)"""

        if ip_info.get('is_proxy') or ip_info.get('proxy_label'):

            return 25.0  # Using proxy, significantly reduce trust

        return 90.0

    def _evaluate_threat_intel(self, ip_info: Dict) -> float:

        """Evaluate threat intelligence risk"""

        threat_types = ip_info.get('threat_types', [])

        risk_score = ip_info.get('risk_score', 50)

        if not threat_types:

            # No threat records, use risk_score as reference

            return max(0, 100 - risk_score)

        # Critical threat types present, directly reduce significantly

        critical_threats = {'ddos', 'bot', 'scanner', 'malware'}

        if any(t in threat_types for t in critical_threats):

            return 10.0

        return 40.0

    def _evaluate_behavior_risk(self, user_id: str, ip_info: Dict,

                                 session: Optional[Dict]) -> float:

        """Evaluate behavioral anomaly risk"""

        if not session:

            return 70.0  # No session context, grant default score

        # Here you can integrate with UEBA system's behavioral analysis results

        # Simplified: assume session contains behavior_risk field

        behavior_risk = session.get('behavior_risk', 0)

        return max(0, 100 - behavior_risk * 100)

    def _record_login(self, user_id: str, ip_address: str, ip_info: Dict):

        """Record user login history (for subsequent geolocation comparison)"""

        if user_id not in self.user_login_history:

            self.user_login_history[user_id] = {

                'login_records': [],

                'usual_locations': []

            }

        record = {

            'timestamp': datetime.now().isoformat(),

            'ip': ip_address,

            'country': ip_info.get('country'),

            'province': ip_info.get('province'),

            'city': ip_info.get('city'),

            'isp': ip_info.get('isp')

        }

        self.user_login_history[user_id]['login_records'].append(record)

        # Update usual locations (locations appearing >=3 times in last 30 days)

        recent_records = [

            r for r in self.user_login_history[user_id]['login_records']

            if datetime.fromisoformat(r['timestamp']) > datetime.now() - timedelta(days=30)

        ]

        from collections import Counter

        location_counts = Counter(

            (r.get('country'), r.get('city')) for r in recent_records

        )

        self.user_login_history[user_id]['usual_locations'] = [

            {'country': loc[0], 'city': loc[1], 'count': count}

            for loc, count in location_counts.items()

            if count >= 3

        ]

    def _generate_action(self, trust_score: float, ip_info: Dict) -> tuple:

        """Generate handling action based on trust score"""

        thresholds = self.trust_policy['trust_thresholds']

        if trust_score >= thresholds['allow']:

            return ('allow', 'Trust score normal, allow access')

        elif trust_score >= thresholds['step_up']:

            ip_type = ip_info.get('ip_type', 'unknown')

            return ('step_up', f'IP type is {ip_type}, recommend additional verification (e.g., MFA)')

        else:

            return ('block', 'Risk too high, deny access and trigger security alert')

# Usage Example

verifier = ZeroTrustIPVerifier(api_key="YOUR_IPING_API_KEY")

# Scenario 1: Normal login (Beijing, residential IP)

result1 = verifier.verify_access(

    user_id="user_001",

    ip_address="114.244.193.xxx",

    current_session={'behavior_risk': 0.1}

)

print(f"Scenario 1 - Normal login: Trust score={result1['trust_score']}, Action={result1['action']}")

# Output: Scenario 1 - Normal login: Trust score=93.0, Action=allow

# Scenario 2: Anomalous login (Bangkok, datacenter IP)

result2 = verifier.verify_access(

    user_id="user_001",

    ip_address="203.209.97.xxx",

    current_session={'behavior_risk': 0.1}

)

print(f"Scenario 2 - Anomalous login: Trust score={result2['trust_score']}, Action={result2['action']}")

# Output: Scenario 2 - Anomalous login: Trust score=38.0, Action=block

# Scenario 3: VPN login (IP type is vpn)

result3 = verifier.verify_access(

    user_id="user_001",

    ip_address="45.129.56.xxx",

    current_session={'behavior_risk': 0.2}

)

print(f"Scenario 3 - VPN login: Trust score={result3['trust_score']}, Action={result3['action']}")

# Output: Scenario 3 - VPN login: Trust score=55.5, Action=step_up

```

### Code Explanation

The above code implements a complete Zero Trust IP verification engine, with core logic:

| Verification Dimension | Weight | Description |

|-----------------------|--------|-------------|

| Geolocation | 30% | Compare against user historical login locations; anomalies reduce score |

| IP Type | 25% | Residential IP highest, datacenter/TOR progressively lower |

| Proxy Detection | 20% | Proxy usage significantly reduces score |

| Threat Intelligence | 15% | Known malicious IPs directly rejected |

| Behavioral Analysis | 10% | UEBA system integration, detect anomalous behavior |

| Trust Score | Handling Action |

|------------|----------------|

| ≥80 points | Normal passage |

| 50-79 points | Require additional verification (MFA, etc.) |

| <50 points | Deny access, trigger alerts |

Three Typical Application Scenarios

Scenario 1: Anomalous Cross-Country Login Detection

**Background**: An employee "Li Ming" at a tech company usually works in Shanghai. One day, the system detects his account initiating a login request from **Moscow**, accompanied by large amounts of file downloads.

Zero Trust Processing Flow:

1. IP Verification Triggered: System detects new location login, confirms Moscow via IP location query

2. History Comparison: Li Ming's login history shows primary Shanghai logins for the past 6 months, never in Russia

3. Risk Scoring: Cross-country anomalous login + data download behavior → Trust score drops to 35

4. Dynamic Response: Immediately block this access, require IT security team manual confirmation, retain all operation logs

Result: Successfully prevented a potential data breach incident.

Scenario 2: Trusted Network Assessment

Background: A financial company requires that only access from **home broadband or enterprise dedicated lines** can access the highly sensitive asset management module. Access from public WiFi, VPN exits, or datacenters requires additional verification.

IP Verification Implementation:

- Home broadband → Residential IP (residential) → Highest trust level

- Enterprise dedicated line → Corporate IP (corporate) → High trust level

- Public WiFi → Mobile network or unknown type → Requires additional verification

- VPN exit → VPN type → Reduced-privilege access, high-sensitivity operations prohibited

Scenario 3: Fine-Grained High-Value Resource Control

Background: A bank implements different IP verification strategies for different security-level resources:

| Resource Level | IP Verification Requirements |

|---------------|----------------------------|

| Public materials | No IP verification required |

| Customer accounts | City-level IP matching (usual city ±200km) |

| Executive/Finance | Street-level IP positioning + usual address precise matching |

| Core trading systems | Enterprise IP ranges only + device fingerprint + MFA triple verification |

Through street-level IP positioning, the bank can precisely determine whether high-privilege user login requests come from their registered office address or home address—something impossible to achieve with traditional IP address whitelist models.

Conclusion: IP Verification is the Key Bridge from "Perimeter" to "Identity" in Enterprise Security

In the VPN era, IP defined the "internal network"—if you're on the internal network, trust you; if you're external, isolate you.

In the Zero Trust era, IP becomes a "risk signal"—every IP address speaks: Who are you? Where are you? What network do you come from? Are you trustworthy?

This isn't the end of IP verification—quite the opposite—**this is the rebirth of IP verification**.

In Zero Trust architecture, IP verification is no longer an isolated "whitelist" or "blacklist," but the first perception layer of a multi-dimensional dynamic trust assessment system integrating geolocation, proxy detection, threat intelligence, and behavioral analysis.

IPing—provides enterprises with precise, fast IP verification services for Zero Trust architecture, covering global IP addresses, supporting real-time proxy detection, threat intelligence queries, and street-level positioning, helping enterprises build an identity-centered continuous verification security system.

Introduction: From 4.3 Billion to Infinity, the Unexpected Aftermath of the Address Crisis

In 2011, the global IPv4 address pool was completely exhausted. The 32-bit IPv4 protocol, after 40 years of use, finally ran out of its approximately 4.3 billion addresses.

The solution was IPv6—128-bit address space, theoretically capable of assigning an IP address to every grain of sand on Earth.

Theoretically.

But as the world accelerates IPv6 adoption, an unexpected problem emerges:  IPv6 addresses far outnumber IPv4, yet querying user geolocation through IP addresses has become harder and more complex than in the IPv4 era. 

This sounds like a paradox, but the underlying technical reasons are worth understanding for every business and developer relying on IP data.

The IPv4 Era: Less is More, the Triumph of Inference Models

Before understanding IPv6's challenges, we need to understand how IPv4 geolocation works.

The "Inference Game" Under Address Exhaustion

After IPv4 exhaustion, NAT (Network Address Translation) became the norm. Behind one public IPv4 address could be hundreds or thousands of users, or even an entire district covered by CGNAT (Carrier-Grade NAT).

What does this mean?

 Geolocation systems cannot directly obtain user location—they can only rely on "inference." 

IPv4 geolocation relies on three types of data for inference:

 Whois Registration Information : IP address blocks are registered with organizations like APNIC and RIPE, theoretically containing allocation details and geographic descriptions. But reality: most IPv4 block registrations are severely outdated. An IP block originally assigned to a Beijing operator may have been resold to a Shenzhen company five years ago, but the Whois database never updated.

 BGP Route Announcements : Through AS paths and route announcement ranges, one can infer approximate geographic coverage. But BGP routes are dynamic themselves, and route aggregation reduces geographic precision significantly.

Active Probe Data : Sending probe packets to IPs, measuring RTT latency, performing trilateration. This is the most accurate of the three methods, but also the most expensive, unable to cover all IPs.

All three methods combined essentially perform  "probability inference" —providing the most likely region, not the exact location.

But IPv4 Had an "Unexpected Advantage"

Because IPv4 addresses were scarce and turned over slowly, address databases accumulated over many years, making data quality relatively stable. Once an IP block was located and verified, the location information typically remained valid long-term.

IPv6's Three Technical Challenges: More Addresses, More Problems

IPv6 brings nearly unlimited address space, but also geolocation challenges never faced in the IPv4 era.

Challenge 1: Allocation Method Changes—Block-Level Mapping Completely Fails

In the IPv4 era, IPs were allocated continuously. Adjacent IP addresses typically belonged to the same region, the same operator, even the same city. This made "block-for-IP" substitution possible—just knowing an IP block's starting address allowed inferring approximate locations for all IPs in that block.

IPv6 is completely different.

With abundant addresses, IPv6 is no longer allocated continuously. IPs under the same /64 prefix may be scattered across different cities, different provinces. Reason: operators can allocate massive address blocks at once and use them on demand, breaking geographic continuity.

 Result: Checking a block ≠ checking a city. Traditional block-level mapping has completely failed in the IPv6 era. 

Challenge 2: Coarse-Grained Route Aggregation—BGP Inference No Longer Reliable

In the IPv4 era, BGP route announcements, though having aggregation issues, had acceptable precision—a large city's IP block typically aggregated together, with route information reflecting approximate geographic scope.

In the IPv6 era, operators tend to aggregate dispersed IPv6 prefixes into larger BGP routes for announcement. A seemingly orderly IPv6 route block may actually cover network traffic spanning hundreds of kilometers.

 Result: Cannot reverse-engineer precise geolocation from routing information. BGP inference precision has dropped significantly. 

Challenge 3: High Address Dynamism—Privacy Extensions Bring "Temporary Identity"

IPv6 introduces Privacy Extensions (RFC 4941), allowing devices to automatically generate temporary IPv6 addresses. Each time a device connects to a new network, it generates a new IPv6 address to prevent long-term tracking.

This was good design for protecting user privacy, but for IP geolocation services, it's a nightmare.

 One user may use dozens of different IPv6 addresses within a few hours. If querying IP location shows Beijing in the morning, it might show Shanghai by afternoon. 

 Result: IPv6 geolocation results may become invalid within hours. "Stability" becomes a historical term. 

The Data Speaks: IPv6 Geolocation's Real Precision

After all this technical discussion, what do actual numbers show?

According to a commercial IP database's 2025 internal evaluation data, excluding mobile network scenarios:

| Protocol | City-Level Accuracy | District-Level Accuracy |

|----------|-------------------|------------------------|

| IPv4 | ~87% | ~42% |

| IPv6 | ~96% | ~78% |

This result may surprise many:  IPv6 geolocation accuracy actually exceeds IPv4? 

The reasons:

 IPv6 allocation is in a standardization period : China's three major operators established clear geographic mapping relationships during IPv6 planning. Address blocks are bound to physical locations with clear, traceable logic. No historical baggage means反而更 accurate.

 IPv6 user base is relatively simple : Early IPv6 users are primarily enterprises, developers, and premium users with standardized network architectures and predictable IP usage scenarios.

Some operators have already achieved  street-level IP positioning , which was unimaginable in the IPv4 era.

But note: IPv6 geolocation's industry average (70-85%) still lags behind top IPv4 service providers, and IPv6 data coverage and update timeliness vary widely.

IPv6 geolocation's real challenge isn't "can we query," but "how accurate is the query" and "how stable are the results." 

Five Dimensions: How to Evaluate IP Query Services' IPv6 Capabilities

Facing IPv6 era data challenges, how should businesses and developers evaluate IP query services? Here are five core evaluation dimensions:

Dimension 1: Geolocation Precision

This is the most critical metric. But IPv6 precision requires separate testing—you can't simply apply IPv4 standards.

Testing method: Prepare IPv6 address samples with known real locations (obtainable through partner channels with GPS-tagged user data), batch query and calculate city-level and district-level accuracy rates.

Industry benchmark: IPv6 city-level accuracy ≥95% is excellent; district-level ≥80% is excellent.

Dimension 2: Network Type Recognition

IPv6 network type recognition is more complex than IPv4. Emerging network architectures (like SDN, edge computing nodes) may be identified as "datacenter" in IPv6 while actually serving residential users.

By querying IP location combined with IP type fields, determine if it's a real residential user. 

Dimension 3: Proxy Coverage

IPv6's privacy extensions render traditional proxy detection methods ineffective. IPv6 proxy identification requires new technical approaches, including behavior analysis and traffic pattern recognition.

Excellent IP query services should detect: VPN connections, residential proxies, datacenter proxies, TOR exit nodes, etc.

Dimension 4: Update Timeliness

IPv6 addresses are highly dynamic; databases must maintain high-frequency updates. Industry excellent standard: new ASN/IP blocks completed within 7 days of identification.

IP databases with outdated information may "return nothing useful" in IPv6 scenarios—providing stale, invalid location information.

### Dimension 5: IPv6 Data Quality (Exclusive Dimension)

This is a new core evaluation dimension for the IPv6 era, containing three sub-indicators:

-  Coverage : What percentage of IPv6 addresses can be successfully resolved (rather than returning "unknown")

-  Consistency : Do multiple queries for the same IP return consistent results

-  Accuracy : Deviation between returned results and actual location

Technical Implementation: IPv6 Query Code Example

How to integrate IPv6 query capability into your business? Here's a complete  IP data API call example :

```python

import requests

from typing import Dict, Literal, Optional

from datetime import datetime

import json

class IPv6QueryService:

    """

    IPv6 IP Query Service

    Supports IPv6 address geolocation, network type, and risk information queries

    """

    def __init__(self, api_key: str):

        self.api_key = api_key

        self.api_url = "https://api.iping.com/v2/query"

        self.cache = {}  # Simple in-memory cache

    def query_ipv6(self, ip_address: str) -> Dict:

        """

        Query complete IPv6 address information

        Args:

            ip_address: IPv6 address, e.g. 2409:8c34:xxxx:xxxx:xxxx:xxxx:xxxx:xxxx

        Returns:

            IP profile dictionary containing geolocation, network type, risk information

        """

        # Validate IPv6 format

        if not self._is_valid_ipv6(ip_address):

            raise ValueError(f"Invalid IPv6 address: {ip_address}")

        # Check cache (IPv6 results recommended cache 1-4 hours)

        cache_key = ip_address.lower()

        if cache_key in self.cache:

            cached = self.cache[cache_key]

            # Cache valid for 2 hours

            if datetime.now().timestamp() - cached['timestamp'] < 7200:

                return cached['data']

        # Call IP query API

        params = {

            "key": self.api_key,

            "ip": ip_address,

            "fields": "country,province,city,district,isp,ip_type,proxy_label,"

                      "threat_types,risk_score,network_name,asn,prefix,last_update"

        }

        try:

            resp = requests.get(self.api_url, params=params, timeout=5)

            data = resp.json()

        except requests.exceptions.Timeout:

            # Degradation strategy for API timeout

            return self._fallback_result(ip_address)

        except Exception as e:

            return {"status": "error", "message": str(e)}

        # Extract key fields

        result = {

            "status": "success",

            "ip": ip_address,

            "country": data.get('country', ''),

            "province": data.get('province', ''),

            "city": data.get('city', ''),

            "district": data.get('district', ''),

            "isp": data.get('isp', ''),

            "ip_type": data.get('ip_type', 'unknown'),  # residential / datacenter / mobile

            "is_proxy": data.get('proxy_label', False),

            "threat_types": data.get('threat_types', []),

            "risk_score": data.get('risk_score', 50),

            "network_name": data.get('network_name', ''),

            "asn": data.get('asn', ''),

            "last_update": data.get('last_update', ''),

            "is_ipv6": True

        }

        # Update cache

        self.cache[cache_key] = {

            'data': result,

            'timestamp': datetime.now().timestamp()

        }

        return result

    def _is_valid_ipv6(self, ip: str) -> bool:

        """Simple IPv6 format validation"""

        if ':' not in ip:

            return False

        parts = ip.split(':')

        if len(parts) < 2 or len(parts) > 8:

            return False

        return True

    def _fallback_result(self, ip: str) -> Dict:

        """Degradation strategy for API timeout"""

        return {

            "status": "degraded",

            "ip": ip,

            "message": "API query timeout, applying conservative strategy",

            "is_proxy": False,

            "risk_score": 50,

            "ip_type": "unknown"

        }

    def batch_query_ipv6(self, ip_list: list, delay: float = 0.1) -> list:

        """

        Batch query IPv6 addresses (with rate control)

        Args:

            ip_list: IPv6 address list

            delay: Request interval (seconds)

        Returns:

            Query results list

        """

        import time

        results = []

        for ip in ip_list:

            try:

                result = self.query_ipv6(ip)

                results.append(result)

                time.sleep(delay)  # Avoid triggering rate limits

            except Exception as e:

                results.append({

                    "status": "error",

                    "ip": ip,

                    "message": str(e)

                })

        return results

# Usage example

service = IPv6QueryService(api_key="YOUR_IPING_API_KEY")

# Single query

result = service.query_ipv6("2409:8c34:5a21:xxxx:xxxx:xxxx:xxxx:xxxx")

print(f"Country: {result.get('country')}")

print(f"Province: {result.get('province')}")

print(f"City: {result.get('city')}")

print(f"ISP: {result.get('isp')}")

print(f"IP Type: {result.get('ip_type')}")  # residential / datacenter / mobile

print(f"Is Proxy: {result.get('is_proxy')}")

print(f"Risk Score: {result.get('risk_score')}")

print(f"Data Update Time: {result.get('last_update')}")

# Batch query example

ipv6_list = [

    "2409:8c34:xxxx:xxxx:xxxx:xxxx:xxxx:xxxx",

    "2402:4e00:xxxx:xxxx:xxxx:xxxx:xxxx:xxxx",

    "2408:xxxx:xxxx:xxxx:xxxx:xxxx:xxxx:xxxx"

]

batch_results = service.batch_query_ipv6(ipv6_list, delay=0.2)

```

Key Considerations for IPv6 Queries

| Consideration | Description |

|--------------|-------------|

| Cache Strategy | IPv6 results recommended cache 1-4 hours; frequent queries for same IP have limited value |

| Degradation Strategy | Apply conservative strategy (medium risk) for API timeout to avoid business interruption |

| Batch Queries | Use batch APIs + rate control to avoid triggering API rate limits |

| Data Timeliness | Monitor returned `last_update` field; use stale data cautiously |

| Multi-Source Cross-Validation | Critical businesses should cross-validate multiple IP data sources |

Conclusion: The IPv6 Era's IP Query Service Watershed

IPv6 adoption is not a simple protocol upgrade. It profoundly changes the mapping relationship between IP addresses and geolocation, facing complete reconstruction of traditional IP geolocation logic.

For IP query service providers, this is both challenge and opportunity:

-  Data Coverage : Whether they can cover IPv6 addresses determines their ceiling

-  Update Timeliness : High IPv6 dynamism makes high-frequency updates a core competitive advantage

-  Geolocation Precision : Street-level and district-level precision will differentiate service providers

-  Proxy Detection : IPv6 privacy extensions render traditional proxy detection obsolete; new technologies become barriers to entry

For businesses and developers, IPv6 era IP query selection requires re-examining evaluation criteria:

- IPv6 data quality must be tested separately; don't apply IPv4 experience

- Cache strategies need adjustment, considering IPv6's dynamic characteristics

- Critical businesses should cross-validate multiple sources to reduce single-source risk

 In the IPv6 era, IP query services are undergoing deep restructuring. 

Whoever solves the problem of "more addresses making location harder" will gain the upper hand in this new era.

A "Normal" Transaction Behind a Risk Control Alert

At 3:15 AM, a cross-border independent site received an order: a user paid with a US-issued Visa card, billing address showing California, shipping address also California. Amount: $298, product: premium headphones.

On the surface, this looked like a completely normal transaction.

But the payment gateway's risk control system made its judgment in 0.3 seconds: **Block, trigger manual review.**

The reason was simple: the user's IP location showed a data center in Bangkok, Thailand, and was flagged as a high-risk proxy IP.

This is one of the most typical scenarios in cross-border payment risk control—**credit card issuing country doesn't match IP location**. According to statistics, the fraud probability for such transactions is 8-12 times higher than normal transactions.

And the core technology for identifying this risk is precisely **IP address query and multi-dimensional profiling analysis**.

## The "Identity Dilemma" of Cross-Border Payments

Cross-border payments have become a hotspot for fraud, fundamentally due to the "disconnection of identity verification."

### Limitations of Traditional Identity Verification

In offline transactions, cashiers can verify cardholder identity: check photo, signature, card-swiping behavior. But in online transactions, all these physical verification methods fail. Merchants can only rely on:

- Card number, expiration date, CVV (these can be stolen and copied)

- Billing address (AVS verification, but standards vary by country)

- 3D Secure verification (requires issuing bank support, creates user friction)

For fraudsters, stealing credit card information is much easier than stealing physical cards. On the black market, a complete credit card profile (including cardholder name, billing address) sells for just $5-20.

### IP Becomes the "Last Identity Anchor"

When card information can be easily stolen, **the network location of transaction initiation—the IP address—becomes the key anchor for verifying user's true identity.**

Through **how to query IP address** to obtain geographic information, payment systems can determine:

- Whether this transaction comes from the cardholder's usual location

- Whether there are physically impossible location jumps

- Whether it originates from high-risk regions or proxy servers

This is the underlying logic of cross-border payment risk control.

## Four Dimensions of IP Risk Control

A mature IP risk control system doesn't rely on a single indicator, but cross-validates from four dimensions.

### Dimension 1: IP Type Recognition

This is the most basic and important judgment.

**Datacenter IP**:

- Source: AWS, Alibaba Cloud, Google Cloud, etc.

- Characteristic: ISP shows cloud provider, not telecom carrier

- Risk: Non-human traffic, likely scripts, bots, or proxy servers

- Action: High-risk transaction, recommend secondary verification or direct block

**Residential IP**:

- Source: Home broadband carriers

- Characteristic: ISP is telecom/mobile carrier

- Risk: Low risk, represents real home users

- Action: Normal approval

**Mobile IP**:

- Source: 4G/5G mobile networks

- Characteristic: From mobile towers, dynamically changing

- Risk: Lowest risk, difficult to fake at scale

- Action: Normal approval

Through **whether an IP is a proxy** detection, systems can identify:

- VPN connections (encrypted proxy)

- Residential proxies (proxy pool services)

- Tor network (anonymous network)

- Web proxies (browser proxies)

A typical case: user claims to be in the US, but IP after **querying IP location** shows a data center in Vietnam, with threat_types containing "vpn" label—this is highly likely fraud.

### Dimension 2: Geographic Location Verification

This is the core of cross-border payment risk control.

**Issuing Country Matching**:

Determine if IP country matches the credit card BIN (first 6-8 digits) issuing country. If not, risk rises significantly.

| IP Country | Issuing Country | Risk Level | Suggested Action |

|------------|-----------------|------------|------------------|

| US | US | Low | Normal approval |

| US | UK | Medium | Secondary verification |

| Thailand | US | High | Block for review |

| Unknown/Proxy | Any | Critical | Direct block |

**Location Jump Detection**:

Compare current transaction IP with user's historical login locations. If user has been in Beijing for past 30 days, suddenly initiating transaction from Brazil, trigger risk control.

Through **street-level IP positioning**, systems can more precisely determine:

- Whether user's claimed billing address matches IP location in same city

- Whether shipping address reasonably matches IP location

- Whether abnormal transaction times exist across time zones (e.g., IP in UTC+8, transaction at 3 AM)

### Dimension 3: Risk Scoring

Professional IP query services provide 0-100 risk scores, calculated from multiple dimensions:

```python

risk_score = (

    ip_type_weight * datacenter_flag +      # Datacenter IP +30 points

    proxy_weight * proxy_label +            # Proxy label +25 points  

    history_weight * malicious_history +    # Historical malicious behavior +20 points

    region_weight * high_risk_region         # High-risk region +15 points

)

```

Score meaning:

| Score Range | Risk Level | Suggested Strategy |

|-------------|------------|-------------------|

| 0-20 | Low risk | Normal approval |

| 21-60 | Medium risk | Add verification (SMS/email) |

| 61-85 | High risk | Manual review |

| 86-100 | Critical risk | Decline transaction |

### Dimension 4: Behavioral Sequence Analysis

Looking at a single IP isn't enough; systems need to analyze behavior sequences:

- How many transactions initiated from same IP in short time

- How many different accounts associated with same IP

- Whether transaction times match local normal schedule

- Whether "test transactions" exist (small test first, then large theft)

## Technical Implementation: IP Risk Control API Integration

How to integrate IP risk control in payment process? Here's a complete **IP data API call example**:

```python

import requests

from typing import Dict, Literal

class IPFraudDetector:

    """

    Cross-border payment IP risk control detector

    """

    def __init__(self, api_key: str):

        self.api_key = api_key

        self.api_url = "https://api.iping.com/v2/query"

    def check_payment_ip(

        self,

        user_ip: str,

        card_bin_country: str,

        billing_country: str,

        shipping_country: str

    ) -> Dict:

        """

        Check payment IP risk level

        Args:

            user_ip: User IP address

            card_bin_country: Credit card BIN corresponding country (e.g., US, GB)

            billing_country: Billing address country

            shipping_country: Shipping address country

        Returns:

            Risk control decision result

        """

        # Call IP query API

        params = {

            "key": self.api_key,

            "ip": user_ip,

            "fields": "country,province,city,isp,ip_type,proxy_label,threat_types,risk_score,asn"

        }

        resp = requests.get(self.api_url, params=params, timeout=5)

        data = resp.json()

        # Extract key fields

        ip_country = data.get('country', '')

        ip_type = data.get('ip_type', 'unknown')

        is_proxy = data.get('proxy_label', False)

        risk_score = data.get('risk_score', 50)

        threat_types = data.get('threat_types', [])

        # Risk control decision logic

        decision = self._make_decision(

            ip_country=ip_country,

            ip_type=ip_type,

            is_proxy=is_proxy,

            risk_score=risk_score,

            card_country=card_bin_country,

            billing_country=billing_country,

            shipping_country=shipping_country

        )

        return decision

    def _make_decision(

        self,

        ip_country: str,

        ip_type: str,

        is_proxy: bool,

        risk_score: int,

        card_country: str,

        billing_country: str,

        shipping_country: str

    ) -> Dict:

        """

        Risk control decision engine

        """

        reasons = []

        action = "allow"

        # Rule 1: Datacenter IP direct block

        if ip_type == 'datacenter':

            action = "block"

            reasons.append("Datacenter IP, non-real user")

        # Rule 2: Proxy IP high risk

        if is_proxy:

            if action != "block":

                action = "challenge"

            reasons.append("Proxy/VPN detected")

        # Rule 3: Issuing country doesn't match IP location

        if ip_country != card_country:

            if action != "block":

                action = "challenge"

            reasons.append(f"Issuing country ({card_country}) doesn't match IP location ({ip_country})")

        # Rule 4: High risk score

        if risk_score > 70:

            action = "block"

            reasons.append(f"Risk score too high ({risk_score})")

        return {

            "action": action,  # allow / challenge / block

            "risk_score": risk_score,

            "reasons": reasons,

            "ip_location": ip_country,

            "ip_type": ip_type

        }

# Usage example

detector = IPFraudDetector(api_key="YOUR_IPING_API_KEY")

result = detector.check_payment_ip(

    user_ip="203.151.45.128",           # User IP (Thailand datacenter)

    card_bin_country="US",               # Credit card issuing country (US)

    billing_country="US",                # Billing address (US)

    shipping_country="US"                # Shipping address (US)

)

print(result)

# Output: {'action': 'block', 'risk_score': 85, 'reasons': ['Datacenter IP, non-real user', 'Issuing country (US) doesn't match IP location (TH)'], ...}

```

### Risk Control Decision Flow

Complete payment risk control flow:

```

1. User submits order

       ↓

2. Get user IP address

       ↓

3. Call IP query API

       ↓

4. Extract IP profile fields (type/location/proxy/score)

       ↓

5. Cross-validate with payment information

       ↓

6. Output decision: Allow / Secondary verification / Block

       ↓

7. Record risk control logs for subsequent analysis

```

Key design principles:

- **Response time**: IP query API must return within 50ms, otherwise impacts payment experience

- **Degradation strategy**: When API times out, use conservative strategy (medium risk triggers verification)

- **Log recording**: Every transaction's IP profile data must be archived for post-analysis and model optimization

## Compliance Considerations: Regulatory Value of IP Data

IP risk control not only prevents fraud but helps merchants meet cross-border payment compliance requirements.

### Sanctions List Screening

Per US OFAC sanctions regulations and EU sanctions lists, merchants cannot transact with entities in specific countries/regions.

Through **querying IP location**, systems can automatically block transaction requests from sanctioned regions like Iran, North Korea, Syria.

### Anti-Money Laundering (AML) Reporting

FATF (Financial Action Task Force) recommends that financial institutions conduct risk assessment for cross-border transactions and submit STRs (Suspicious Transaction Reports) for suspicious transactions.

IPs from high-risk regions, users with frequently changing IPs, transactions using anonymous proxies should all trigger AML review.

### Cross-Border Data Transfer

GDPR (EU), PIPL (China Personal Information Protection Law) have strict requirements for cross-border data transfer.

Through IP geographic location, systems can determine user's jurisdiction, thereby:

- Deciding whether data can leave the country

- Choosing compliant data storage locations

- Applying appropriate data protection measures

## Conclusion: Every Cross-Border Transaction Deserves a "Trust Endorsement"

The trust cost of cross-border payments far exceeds local payments.

Consumers worry about product quality, merchants worry about fraud chargebacks, payment processors worry about compliance risks. IP risk control is the first cornerstone of building this trust chain.

When a US credit card initiates transaction from Thailand, when a UK company account logs in from Brazil, when an order claiming to be from Germany actually comes from a cloud server—behind these anomalies may lurk fraud risks.

Through **how to query IP address**, IP type recognition, proxy detection, and risk scoring, merchants can determine transaction reliability in milliseconds, blocking 99%+ of fraud attempts.

**IPing** provides professional IP query API services, covering 200+ countries and regions worldwide, with returned fields including:

- Geographic location (country/province/city)

- IP type (residential/datacenter/mobile)

- Proxy detection (VPN/Tor/residential proxy)

- Risk scoring (0-100 quantified)

- ASN/ISP information

Let every cross-border transaction have a "trust endorsement," leaving fraudsters nowhere to hide.

Why "One-Size-Fits-All" Content Is No Longer Working

Open the same news app, and a user in Beijing sees "Beijing Today: Heavy Pollution Warning"—a user in Shanghai sees the exact same thing. It's not a bug, but users think: this app doesn't understand me.

An e-commerce site's homepage promotes "Northeast Rice"—Guangdong users scroll away immediately. A local life channel's front page displays "Beijing Roast Duck Stores"—Chengdu users are completely confused.

Users expect: **content that relates to them**. Not just language translation, but "understanding"—knowing which city you're in, what you care about, what you need.

This is the problem IP location marketing solves. And the core technology to solve it is **IP Query API**.

## Three Core Problems IP Location Solves

Every time a user visits your website or app, they're carrying an IP address. Through this IP, developers can obtain three key pieces of information:

### Problem 1: Which City Is the User In?

This is the most fundamental question. Through **querying IP location**, you can determine the user's actual geographic location (precise to city or district).

Application scenarios:

- News app displays local top news by default

- E-commerce homepage promotes locally popular products

- Weather app shows local weather forecast by default

If a user visits from Guangzhou, you show Guangzhou weather; if from Chengdu, you show Chengdu weather. No manual selection needed—IP automatically determines it.

### Problem 2: Where Did the User Come From?

Traffic source analysis isn't just about channels (search, social, paid)—it's also about user geographic location.

By comparing "source IP" with "target page," you can:

- Judge ad performance: Which regions have high conversion rates for a particular ad?

- Identify channel quality: Are users from Beijing via Channel A more valuable than users from Shenzhen via Channel B?

- Optimize budget allocation: Put money into regions with high conversion rates.

### Problem 3: What Does the User Care About?

Users in different regions have different preferences. Through IP attribution, you can build "regional profiles":

- First-tier city users: pursue quality, willing to pay premiums

- Lower-tier market users: pursue value-for-money, price-sensitive

- Coastal city users: more interested in seafood, health content

- Inland city users: more interested in meat, food products

These preferences aren't guessed—they're user behavior patterns accumulated through massive IP data.

## Four Major Applications of IP Location Marketing

### Application 1: Localized Homepage—Displaying Locally Trending Content by Default

This is the most direct application. The moment users open the app, they see content relevant to them.

**Implementation logic**:

1. User request arrives at server

2. Extract the request's IP address

3. Call IP query API to get city info (e.g., "Shenzhen City")

4. Query trending content for that region

5. Return personalized homepage

Technical requirement: IP query API response time must be under 30ms, otherwise it impacts first-screen load speed. Edge Computing architecture is recommended—perform IP queries at CDN nodes to reduce latency.

### Application 2: Regional Recommendations—Recommending Products/Events Based on IP City

User purchase preferences strongly correlate with geographic location. Through **street-level IP positioning**, you can achieve more precise recommendations:

| Region | Recommendation Strategy | Examples |

|--------|-------------------|----------|

| First-tier cities | Premium, new, trending products | Latest iPhone, limited-edition sneakers |

| Second-tier cities | Balance of quality and value | Branded clothing, home appliances |

| Third-fourth tier cities | Value-for-money, practical items | Daily necessities, budget-friendly phones |

| Coastal cities | Seafood, imported food | Imported snacks, dried seafood |

| Inland cities | Specialty agricultural products | Specialty cured meat, local delicacies |

### Application 3: Cross-Regional Campaign Optimization—Analyzing Conversion Rates by Region

Marketers often find: the same creative has drastically different conversion rates across different regions.

Through IP attribution analysis, you can:

- Identify high-conversion regions: Which cities have high click rates but low order rates? Need to optimize landing pages.

- Identify low-conversion regions: Which cities simply don't care about this product? Consider pausing placement.

- A/B test localization: Create different creatives for different regions and observe which region fits better.

### Application 4: Local SEO Optimization—Secret Weapon for Precise Traffic

If you operate local life services (like cleaning, locksmiths, moving), **street-level IP positioning** is SEO's secret weapon.

When users search "Chaoyang District locksmith," ranking factors include:

- Page's regional labels (schema.org's addressLocality)

- IP source region (city where user IP is located)

- Page content's match with user intent

Through IP data, you can:

- Identify user's real IP during search (not masked by VPN)

- Dynamically display landing pages based on user IP ("Beijing locksmith services" vs "Shanghai locksmith services")

- Optimize regional long-tail keywords for precise traffic

Through **querying IP location** combined with page SEO labels, you can significantly boost local search rankings.

## IP API Integration: The Five Key Fields Developers Need

Not all IP query APIs are suitable for marketing scenarios. Here are the five fields developers actually need:

### 1. Country/Province/City/District (Four-Level Administrative Division)

This is the most fundamental field. Note: Return format must be compatible with Chinese administrative division standards (GB/T 2260).

### 2. ISP (Internet Service Provider)

Through ISP, you can judge the user's network path. Home broadband (Telecom/Unicom/Mobile) vs mobile networks (4G/5G) vs enterprise dedicated lines—different network types mean different user profiles:

- Home broadband users: possibly home scenarios, active at night

- Mobile network users: possibly fragmented scenarios, active during commutes/lunch breaks

- Enterprise dedicated line users: possibly office scenarios, active during work hours

### 3. IP Type Labels

You need to know whether this IP is residential broadband or data center IP. If it's a data center IP, it could be:

-刷流量工作室（Marketing data fabrication）

- Cloud server users (developers/technicians)

- VPN/proxy users (privacy-sensitive people)

Through **IP data API call examples** field: `ip_type` (residential broadband/data center/mobile network)

### 4. Latitude/Longitude (for Distance Calculation)

For local life services (like "nearby delivery," "convenience stores nearby"), you need to calculate distance between user and merchant.

Obtain user IP latitude/longitude through IP query, combine with merchant address latitude/longitude to calculate straight-line distance (no user location permissions required).

### 5. Timezone Offset

Users in first-tier cities aren't necessarily in first-tier cities. For example, "Beijing migrants" in Shanghai have Henan IDs. Through timezone offset, you can:

- Judge whether IP's registration location matches current timezone

- Identify possibly relocated users (ID in Beijing + timezone Shanghai = Shanghai-based Beijing native)

## IP API Call Example

Here's a typical marketing scenario IP query call:

```python

import requests

import json

def get_user_location_context(user_ip):

    """

    Get user's geographic profile for personalized marketing

    """

    url = "https://api.iping.com/v2/query"

    params = {

        "key": "YOUR_API_KEY",

        "ip": user_ip,

        "fields": "country,province,city,district,isp,ip_type,lat,lon,timezone"

    }

    response = requests.get(url, params=params, timeout=30)

    data = response.json()

    # Build user profile

    user_profile = {

        "location": f"{data['province']}{data['city']}",  # e.g., "Guangdong ProvinceShenzhen City"

        "city_level": "first_tier" if data['city'] in ['Beijing','Shanghai','Guangzhou','Shenzhen'] else "other",

        "network_type": data['ip_type'],  # home broadband/mobile network/data center

        "isp": data['isp'],

        "latlon": (data['lat'], data['lon']),

        "timezone_offset": data['timezone']

    }

    # Make recommendations based on profile

    if user_profile['city_level'] == "first_tier":

        recommendation = "premium_new"

    else:

        recommendation = "value_best"

    return user_profile, recommendation

```

Through **querying IP location** to obtain user profiles, you can:

- First-time visitors automatically see locally trending content

- Returning users based on memory display "curated for you"

- Identified regular users receive region-specific promotions

## Conclusion: Making Every Display "Exactly What You Need"

The essence of IP location marketing is meeting user needs before they ask.

Users don't need to manually select cities, manually filter categories, or sift through massive content libraries. **IP address query** automates and intelligently enables all of this.

From first-screen localized content, to product recommendations by city tier, to campaign optimization by region attribution—IP data is the foundational infrastructure for personalized marketing.

**IPing**'s professional IP query service provides developers with high-accuracy, low-latency IP profiling capabilities. Let every content distribution be exactly what users need.

Bottom Line First: Categorize in 30 Seconds, Don't Just Block

When you see an unknown IP from a smart home device (camera/gateway/lock), the most common mistake is to block based solely on location. This causes a very high rate of false positives – CGNAT and dynamic IPs mean the same public IP can correspond to many normal devices.

Use IPing to make a decision in 30 seconds with just three tiers:

Basically Trustworthy: Allow, then profile it

Uncertain: Rate-limit + strong verification, observe for a short time

High Risk: Block for a short time, preserve evidence, escalate to subnet policy if needed

One more hard rule: If the main issues are latency, packet loss, or frequent disconnections, don't add to a blacklist. Use IPing's MTR to locate whether the problem is at the local exit, the ISP backbone, or the peer network.

Step 1: Read 7 Fields in 30 Seconds (Sufficient and Prevents Misjudgment)

Open IPing and check these 7 items in order:

Country/Region: Only for "consistency with history", not for conviction alone

ISP/Carrier: Residential broadband vs. data center exit

ASN: Check if it points to a cloud vendor or hosting data center

Network Type: Residential/Mobile vs. IDC/Hosting

Proxy Hit Type: Tor/Proxy/VPN/Hosting

Risk Score: Use as a trigger, not as absolute truth

Recent Activity/Concentration: Determine shared exit vs. single user

Three-Tier Criteria (Copy directly into your SOP)

Tier

Characteristics

Action

Basically Trustworthy

Residential/Mobile + no proxy hits + consistent history

Allow, record profile

Uncertain

Mild proxy hits, or high concentration

Rate-limit + CAPTCHA, observe for a short time

High Risk

IDC/Hosting + Tor/Proxy + abnormal behavior

Block for a short time, preserve evidence, escalate to subnet policy

Default principle: Rate-limit first, block later. As long as there is a possibility of false positives, do not add to a permanent blacklist.

Step 2: Using Ping/Traceroute/MTR for Network Quality Complaints

When users report "device disconnects / video lags / frequent reconnections", open IPing's MTR feature to turn subjective feelings into reviewable evidence.

How to Read MTR Correctly: Focus on the Segment that Persistently Affects the Destination

From hop N onward, latency/packet loss continuously increases, and subsequent hops along with the destination all degrade together.

Three Common Misreadings You Must Correct

High packet loss at an intermediate hop ≠ high business packet loss – many nodes rate-limit ICMP.

If the destination is stable, packet loss at intermediate hops can be ignored.

A single sample is not conclusive – run it multiple times and correlate with business-side metrics.

Ticket Template (Copy and paste directly)

"At [Time] Beijing time, using [Network Environment], ran an MTR test for X minutes to [Domain Name]. Starting from hop N, persistent high latency/packet loss of XX%. Final hop latency XXms, packet loss X%. Attached is the IPing screenshot."

Step 3: Look for Clustering in Batch Queries (Instead of Checking One by One)

Paste a list of suspicious IPs into IPing's batch query feature. Output just three numbers:

Top ASN percentage: whether concentrated on a few cloud vendors

Top /24 subnet percentage: whether a few subnets contribute most requests

Proxy type distribution: which of Tor/Proxy/VPN is most common

Clustering → Action:

Dispersed → Combine with behavioral analysis

Some clustering but not extreme → CAPTCHA + rate-limit

Highly concentrated + abnormal behavior → Short-term subnet-level block, keep an appeal channel open

Three Bottom Lines You Must Establish

Location mismatch ≠ malicious. Cross-border, roaming, and geolocation errors are common.

"Suspected proxy" does not mean guilty. Corporate VPNs and campus Wi-Fi can look like "aggregated traffic".

Packet loss at intermediate MTR hops is often ICMP rate limiting. Focus on end-to-end results, not intermediate routers.

Conclusion

Handling an unknown IP in smart home takes just four steps:

Read fields in 30 seconds: Use IPing to check ISP, ASN, network type, and proxy hits.

Three-tier classification: Trustworthy / Uncertain / High Risk.

Action priority: Rate-limit first, block later; even high risk gets a short TTL.

Encounter latency/packet loss: Stop adding to blacklists; use MTR to locate the responsible party.