Detecting Tor Exit Nodes with Subnet Clustering

Tor (The Onion Router) plays a vital role in online privacy, but it also creates blind spots for businesses trying to defend against fraud, abuse, and anonymity-based attacks. Detecting Tor exit traffic is essential—but most detection methods are blunt and outdated. CandycornDB takes a more sophisticated approach by analyzing subnet clustering and ASN behavior to detect Tor exit nodes faster and more reliably.

Why Basic Tor Lists Aren’t Enough

Most security teams rely on public Tor node lists to block or flag traffic. These include static IPs collected by crawling Tor consensus directories. The problem?

• Public lists are often **outdated or incomplete**
• Some Tor nodes **mask their behavior** or rotate IPs rapidly
• Relying on exact matches leads to **false negatives** (missed threats)

Meanwhile, false positives (flagging users who simply share IP space) frustrate legitimate customers and damage trust.

CandycornDB’s Subnet-Aware Detection Model

Rather than treating Tor IPs as isolated, static entities, CandycornDB analyzes their behavior across entire network segments. We cluster IPs by:

• Subnet proximity: Grouping IPs in the same /24, /22, or /20 block
• ASN history: Scoring Autonomous Systems based on recurring Tor-like behavior
• Traffic patterns: Identifying scanning, rotation, and anonymization trends across adjacent IPs

This approach enables CandycornDB to surface previously unlisted Tor-like IPs with similar behavior, often before they show up in public directories.

Real-Time, Risk-Weighted Scoring

Instead of a binary flag (“Tor” or “Not Tor”), we assign a **risk score** to every IP based on:

• How closely its behavior matches known exit nodes
• The reputation of the surrounding subnet
• Historical roles of the ASN (e.g., abuse-prone cloud hosts)

Security and fraud teams can then decide how to treat that IP—block it, flag it, or simply log it for deeper review. The goal is control, not overreaction.

Advantages Over Public Lists

CandycornDB offers a real-time, intelligence-led advantage by:

• Detecting Tor exit node behavior **before it appears on blocklists**
• Scoring unknown or spoofed traffic using **network context**
• Reducing false positives that harm legitimate privacy users

We believe in defending against anonymity abuse—not privacy itself. Our subnet analysis helps teams make smarter, more nuanced decisions at scale.

Start Detecting Smarter

If your current fraud stack is still using raw IP blocklists to detect Tor traffic, you’re not seeing the whole picture. CandycornDB is purpose-built to help you:

• Detect more threats
• Reduce false positives
• Respond faster with real-time scoring

View our API docs or sign up now to start detecting anonymized traffic with subnet-aware IP intelligence.