Splitting Hairs and Network Traces: Improved Attacks Against Traffic Splitting as a Website Fingerprinting Defense

Abstract

The widespread use of encryption and anonymization technologies---e.g., HTTPS, VPNs, Tor, and iCloud Private Relay---makes network attackers likely to resort to traffic analysis to learn of client activity. For web traffic, such analysis of encrypted traffic is referred to as Website Fingerprinting (WF). WF attacks have improved greatly in large parts thanks to advancements in Deep Learning (DL). In 2019, a new category of defenses was proposed: traffic splitting, where traffic from the client is split over two or more network paths with the assumption that some paths are unobservable by the attacker. In this paper, we take a look at three recently proposed defenses based on traffic splitting: HyWF, CoMPS, and TrafficSliver BWR5. We analyze real-world and simulated datasets for all three defenses to better understand their splitting strategies and effectiveness as defenses. Using our improved DL attack Maturesc on real-world datasets, we improve the classification accuracy wrt. state-of-the-art from 49.2% to 66.7% for HyWF, the F1 score from 32.9% to 72.4% for CoMPS, and the accuracy from 8.07% to 53.8% for TrafficSliver BWR5. We find that a majority of wrongly classified traces contain less than a couple hundred of packets/cells: e.g., in every dataset 25% of traces contain less than 155 packets. What cannot be observed cannot be classified. Our results show that the proposed traffic splitting defenses on average provide less protection against WF attacks than simply randomly selecting one path and sending all traffic over that path.