The Engineering Complexity of Accurate VPN and Proxy Detection
Building an in-house solution to detect anonymizers is often the first instinct for engineering teams facing fraud or scraping attacks. However, the landscape of IP obfuscation has evolved significantly beyond static datacenters. This post explores why simple blocklists fail and the technical overhead required to maintain high-fidelity detection.
1. The Volatility of IPv4 Reputation
The primary challenge is the churn rate of IP ownership. Static lists of "bad IPs" degrade in value within hours, not days.
- Cloud Recycling: AWS, GCP, and Azure recycle elastic IPs aggressively. An IP used by a VPN provider today might be assigned to a legitimate SaaS webhook tomorrow.
- DHCP Churn: ISP users (residential) are frequently reassigned IPs. A permanent ban on a dynamically assigned IP will eventually result in false positives against legitimate users.
2. Residential Proxies and the ASN Illusion
Traditional detection relies on flagging Autonomous System Numbers (ASNs) associated with hosting providers (e.g., DigitalOcean, Hetzner). This logic fails against Residential Proxies (ResIPs).
ResIP networks route traffic through compromised IoT devices or users who have opted into SDKs (like free VPN apps) in exchange for service. The traffic exits through a legitimate ISP connection (e.g., Comcast, AT&T).
The Engineering Challenge: You cannot simply block the ASN. You must distinguish between a NAT gateway servicing a family and a NAT gateway servicing a proxy node.
Conceptual Detection Logic (Python)
To detect ResIPs, you need to correlate the IP against known proxy subnets or analyze open ports indicative of proxy services, rather than just ASN types.
def assess_risk(ip_metadata):
"""
Simple heuristics fail against Residential Proxies.
A manual implementation requires real-time port scanning data.
"""
# 1. Check ASN Type
is_hosting = ip_metadata.get('asn_type') == 'hosting'
# 2. Check for Proxy Ports (SOCKS5, HTTP Proxy)
# This data is hard to acquire without active probing (honeypots)
open_proxy_ports = ip_metadata.get('open_ports', [])
has_proxy_signature = any(p in [1080, 8080, 3128] for p in open_proxy_ports)
if is_hosting:
return "High Risk (Datacenter)"
elif has_proxy_signature:
# This detects the Residential Proxy
return "High Risk (Residential Proxy)"
return "Low Risk"
3. TCP/IP Stack Fingerprinting
Sophisticated detection goes beyond the IP address. It involves Passive OS Fingerprinting (p0f). VPNs often alter the TCP/IP packet header parameters (TTL, Window Size, MSS) in ways that create a mismatch with the User-Agent header sent by the browser.
- MTU Mismatches: Tunneling protocols (OpenVPN, WireGuard) introduce overhead, often lowering the Maximum Transmission Unit (MTU). If a request claims to be from a standard Chrome browser on Windows but the MSS suggests an MTU of 1300, it is likely encapsulated.
- OS Mismatch: If the User-Agent says "iPhone" but the TCP signature looks like Linux (often used by proxy servers), the request is synthetic.
4. CGNAT and False Positives
Carrier-Grade NAT (CGNAT) is prevalent in mobile networks. A single public IPv4 address may represent thousands of legitimate users.
Naive implementation of rate-limiting or IP banning on CGNAT ranges results in massive collateral damage. Accurate detection requires maintaining a database of mobile gateway ranges to apply looser blocking rules, forcing reliance on device fingerprinting rather than IP reputation for these segments.
FAQ
Q: Can't I just use a GeoIP database? A: No. GeoIP tells you where an IP is, not what it is. A VPN server in New York looks geographically identical to a legitimate user in New York.
Q: How do we handle iCloud Private Relay? A: Apple publishes the egress ranges for Private Relay. These should generally be treated as "low trust" but not necessarily malicious, depending on your risk tolerance. They are technically proxies but authenticate valid Apple users.
Q: Why not active probing? A: Active probing (scanning the incoming IP for open proxy ports) adds latency to the user request and can trigger abuse complaints from ISPs. Passive analysis via a dataset provider is faster and safer.
Scale Your Security with IPASIS
Maintaining a real-time database of millions of proxy nodes, calculating TCP deviations, and tracking residential churn is a dedicated infrastructure challenge.
IPASIS provides a single, low-latency API endpoint to detect VPNs, proxies, and TOR nodes with high fidelity. Stop building blocklists and start analyzing intelligence.