k-fingerprinting: a Robust Scalable Website Fingerprinting Technique

TL;DR

This paper introduces k-fingerprinting, a scalable website fingerprinting method using random decision forests that outperforms existing techniques, even with noisy data and defenses, achieving high accuracy on large datasets.

Contribution

The authors propose k-fingerprinting, a novel scalable attack method based on random decision forests, demonstrating superior performance over prior attacks against encrypted web traffic and defenses.

Findings

Achieves 85% TPR on 30 monitored services from 100,000 pages.

Maintains low FPR of 0.02% in large-scale settings.

Effectively operates despite noisy data and defenses.

Abstract

Website fingerprinting enables an attacker to infer which web page a client is browsing through encrypted or anonymized network connections. We present a new website fingerprinting technique based on random decision forests and evaluate performance over standard web pages as well as Tor hidden services, on a larger scale than previous works. Our technique, k-fingerprinting, performs better than current state-of-the-art attacks even against website fingerprinting defenses, and we show that it is possible to launch a website fingerprinting attack in the face of a large amount of noisy data. We can correctly determine which of 30 monitored hidden services a client is visiting with 85% true positive rate (TPR), a false positive rate (FPR) as low as 0.02%, from a world size of 100,000 unmonitored web pages. We further show that error rates vary widely between web resources, and thus some patterns of use will be predictably more vulnerable to attack than others.