DeTorrent: An Adversarial Padding-only Traffic Analysis Defense
James K Holland, Jason Carpenter, Se Eun Oh, Nicholas Hopper

TL;DR
DeTorrent is a neural network-based traffic padding defense that effectively reduces website fingerprinting and flow correlation attack success rates with moderate overhead, enhancing user privacy in anonymity networks like Tor.
Contribution
DeTorrent introduces a novel adversarial neural network approach for traffic padding that outperforms existing defenses against WF and FC attacks without significant latency.
Findings
Reduces attacker's accuracy by 61.5% in WF settings.
Cuts true positive rate for FC attacks to 0.12 at low false positive rate.
Maintains effectiveness when deployed on live Tor traffic.
Abstract
While anonymity networks like Tor aim to protect the privacy of their users, they are vulnerable to traffic analysis attacks such as Website Fingerprinting (WF) and Flow Correlation (FC). Recent implementations of WF and FC attacks, such as Tik-Tok and DeepCoFFEA, have shown that the attacks can be effectively carried out, threatening user privacy. Consequently, there is a need for effective traffic analysis defense. There are a variety of existing defenses, but most are either ineffective, incur high latency and bandwidth overhead, or require additional infrastructure. As a result, we aim to design a traffic analysis defense that is efficient and highly resistant to both WF and FC attacks. We propose DeTorrent, which uses competing neural networks to generate and evaluate traffic analysis defenses that insert 'dummy' traffic into real traffic flows. DeTorrent operates with moderate…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Adversarial Robustness in Machine Learning · Hate Speech and Cyberbullying Detection
