This analysis was inspired by insights from a free Black Hat webinar with IPinfo, “VPN and Proxy Detection: Interpreting and Applying IP-Level Data Responsibly,” originally aired Tuesday, July 29 at 2 PM PDT.

In today’s rapidly evolving digital landscape, one of the most critical yet complex challenges facing organizations today is understanding who is truly behind an IP address. As digital threats increase in sophistication and an increased usage in security technology in everyday life, the ability to distinguish between legitimate users and malicious actors requires thorough, intelligent analysis.

1. Anatomy of Modern IP Privacy Technologies

1.1 Virtual Private Networks (VPNs)

VPNs remain the most recognizable privacy technology, with usage surging across both consumer and enterprise contexts. From a security perspective, VPNs present a dual challenge: they can be used legitimately by employees working remotely or individuals seeking privacy protection, but they also serve as a primary tool for bypassing geographical restrictions and masking malicious activity.

There are different approaches to detecting VPN traffic, this can be performed by correlating traffic patterns to maintained lists of known exit nodes. However, the rapid growth of commercial VPN market means that new services emerge regularly, requiring continuous monitoring and attribution efforts.

IP behavioral analytics is highly beneficial when paired with VPN or proxy data. For example, if traffic is coming from a masked VPN known to be used by threat actors, and that VPN’s IPs frequently appear across hundreds of login attempts, that’s a red flag worth acting on.

1.2 Residential Proxy Networks

A concerning development in the privacy technology space is the rise of residential proxy networks. These services route traffic through legitimate residential IP addresses, making detection significantly more challenging. Unlike VPN exit nodes, which typically operate from data centers, residential proxies use IP addresses that belong to actual home internet connections.

Observing indicators of IP intelligence use have major implications in the context of security. For instance, an IP address that appears to belong to a residential user in Seattle could be routing traffic from anywhere in the world. This capability can completely bypass traditional geolocation-based security controls and fraud detection systems.

1.3 Apple Private Relay and Similar Services

Many of the largest tech companies have introduced privacy solutions that occupy a middle ground. Apple’s Private Relay, for instance, aims to preserve approximate geolocation while obscuring the user’s specific IP address. These services present complex security challenges as they maintain some level of geographical accuracy while providing a degree privacy protection.

2. Challenges in IP Detection

Developing effective IP intelligence is a multi-dimensional challenge. Here we review several common challenges in IP intelligence.

2.1 The Dynamic Nature of IP Addresses

IP addresses don’t maintain static classifications. A residential IP might join a proxy network, serve as a VPN exit node for a period, then return to normal residential use. IPinfo reported in a July 2025 webinar BlackHat that privacy-related IP classifications can change for 7-56% of addresses monthly, making real-time, continuous detection systems a security essential.

2.2 Attribution Complexity

Identifying that an IP address belongs to a VPN service is only the first step. Apart from individual devices, attribution to specific providers requires sophisticated monitoring infrastructure, such as maintaining accounts across hundreds of services and continuously mapping their exit node infrastructure. This cat-and-mouse game demands significant technical resources and expertise.

2.3 False Positive Management

A highly challenging aspect of IP intelligence is balancing detection accuracy with user experience. For instance, a legitimate employee using a VPN can typically resolve access issues by disabling their VPN. However, a user whose residential IP has been flagged as part of a proxy network has no resort—residential IP users have no control over the IP proxy pool service.

3. Practical Applications of IP Intelligence

3.1 E-Commerce
Geolocation & IP reputation checks identify suspicious mismatches, such as a billing address conflicting with the user’s detected location, enabling highly nuanced fraud screening with contextual data.

3.2 Streaming
Real‑time VPN/proxy detection from IP intelligence services enable streaming platforms to enforce geo‑licensing agreements effectively, ensuring access only from allowed regions.

3.3 Security
Anonymized or masked logins (via VPNs, proxies) are flagged early, triggering risk-based responses like CAPTCHAs or additional verification instead of outright blocking.

3.4 Bot Defense
Datacenter IPs and anonymizing services are heavily used in scraping and bot attacks. IP intelligence helps differentiate these from residential or legitimate activity.

4. Key Signals in IP Intelligence

IP intelligence privacy research is a multi-layer process starting from key detection signals including public lists, probe network scans, VPN handshakes, open ports, suspicious activity, IP registration data, and hosting provider characteristics (ASNs, hosted domains, and port scans) to ensure highly accurate privacy and hosting detection.

5. Critical IP Detection Signals

5.1 Public lists (e.g. TOR)
Public lists of known anonymity networks, such as TOR, remain a hotbed for ransomware today (CISA.gov, 2025). However, federal security organizations have reported residential proxy networks can often evade detection, especially when enriched with advanced contextual signals (CISA.gov, 2024).

5.2 Direct service scans
Real-time service scanning is a valuable technique. Security firm GreyNoise show recent dramatic spikes in VPN system scanning, such as a surge targeting zero-day VPN endpoints (GreyNoise, 2024), highlighting active monitoring as a crucial detection signal.

5.3 VPN handshake responses
Deep packet inspection and protocol fingerprinting remain relevant and powerful security techniques. Academic researchers from ACM mention that “network traffic fingerprinting”, a passive traffic analysis followed by active probing, can detect OpenVPN usage with over 85% accuracy, even when obfuscation layers are present (Communications of the ACM, 2024).

5.4 Open ports associated with VPN services
Advanced port scanning techniques may reveal distinguishing behaviors, such VPN-related ports. Recent advances in network security research demonstrate advanced port scanning capabilities using DPDK-based scanners to improve the visibility and speed of identifying such signals (PMC Digital Health, 2023).

5.5 Suspicious device activity
Detection is increasingly behavioral, signaling beyond static indicators. Recent security research has demonstrated that highly tailored machine learning models can be leveraged to achieve high accuracy in distinguishing VPN traffic based solely on traffic behavior patterns (arXiv, 2025).

5.6 WHOIS association with VPN providers
WHOIS data still helps link IP registrations to known VPN services. However, as reported by security firm DigitalElement, attribution complexity has increased and overly simplistic approaches, like blocking all VPNs or allowing them unchecked, create blind spots. Cyber mercenaries often hide behind no-log VPNs to evade detection, while legitimate, privacy-forward users get caught in the crossfire. (DigitalElement, 2024)

5.7 Hosting signals (hosted domains, “pingability”, ASN characteristics, etc.)
Enriched IP intelligence analysis leverages metadata for hosted domains, ASN behavior, and network infrastructure for high-fidelity classification (IPinfo, 2024). Multi-factor signal analysis is instrumental in distinguishing between residential, proxy, hosting, and anonymizing infrastructure.

6. IPinfo’s Key Takeaways and Recommendations

IPinfo’s key takeaways, as described on their blog post covering their Black Hat webinar.

6.1. VPNs and proxies are not inherently malicious.
Commercial VPNs, residential proxies, and hosting relays all operate differently in IP-level signals. Each have unique risks, legitimacy, and detection nuances.

6.2. IP signals are context dependent.
An IP alone will not tell you everything, but layered signals build a vivid illustration. Dynamic IP intelligence considers continuously changing indicators of compromise, and where static detection methods fall short.

6.3. Anonymization has no universal threshold.
Every detection threshold has varied uses. Whether one is trying to block abuse, flag suspicious behavior, or simply enrich logs with more metadata, understanding your tolerance for false positives is key.

6.4. Residential proxies are the most difficult to detect.
Residential proxy networks attempt to mimic legitimate home traffic, making detection a distinct challenge from typical VPNs, however, this type of activity can be discovered at scale with data-driven behavioral analysis and continuous, active probing.

6.5. Anonymized IP detection easily fits in a tech stack.
IP Intelligence APIs, such as IPinfo, supports existing workflows commonly found in today’s industry. Enterprise tools like Splunk, Google Cloud, Snowflake, and Microsoft Security Copilot support IPinfo data integrations to flag anonymous VPN traffic and improve security risk detection.

7. Conclusion

IP intelligence is a critical but often overlooked component of modern cybersecurity infrastructure. As privacy technologies continue to evolve and proliferate, organizations must develop thorough approaches to handling the volatile nature of modern network traffic.

Today’s security professionals and researchers should consider IP intelligence as a multifaceted and continuously-evolving topic. State-of-the-art privacy technologies are leveraging this sector of intelligence when analyzing new security risks. IP intelligence is critical to making informed decisions about security risks in an increasingly complex network landscape.

Thank you so much for reading! Interested in securing the future of our digital estate? Consider our Cybersecurity & Networking course offerings at Claremont Graduate University, Center for Information Systems & Technology. →