DeTorrent：一种仅使用对抗性填充的流量分析防御方法
DeTorrent: An Adversarial Padding-only Traffic Analysis Defense
AI总结：
DeTorrent 利用竞争神经网络生成并评估插入虚拟流量的对抗性填充防御，在中等开销下显著降低网站指纹识别和流关联攻击效果，并在 Tor 实时流量中验证了实用性。
摘要：
尽管 Tor 等匿名网络旨在保护用户隐私，但它们容易受到网站指纹识别（Website Fingerprinting，WF）和流关联（Flow Correlation，FC）等流量分析攻击。近期实现的 WF 和 FC 攻击（如 Tik-Tok 和 DeepCoFFEA）表明，这些攻击能够被有效实施，从而威胁用户隐私。因此，迫切需要有效的流量分析防御手段。现有防御方法多种多样，但大多数要么效果不佳，要么带来高延迟和带宽开销，要么需要额外的基础设施。为此，我们旨在设计一种高效且对 WF 和 FC 攻击均具有高度抵抗力的流量分析防御方法。我们提出 DeTorrent，该方法利用相互竞争的神经网络来生成和评估流量分析防御方案，在真实流量流中插入“虚拟”流量。DeTorrent 在中等开销下运行，且不会延迟流量。在封闭世界 WF 设置下，它将攻击者的准确率降低了 61.5%，比次优的仅填充防御方法高出 10.5%。针对最先进的 FC 攻击者，在误报率为 $10^{-5}$ 时，DeTorrent 将真正率降至约 0.12，不到次优防御方法的一半。我们还通过将 DeTorrent 部署在 Tor 网络旁证明了其实用性，并发现它在应用于实时流量时仍能保持其性能。
英文摘要：
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 overhead and without delaying traffic. In a closed-world WF setting, it reduces an attacker's accuracy by 61.5%, a reduction 10.5% better than the next-best padding-only defense. Against the state-of-the-art FC attacker, DeTorrent reduces the true positive rate for a $10^{-5}$ false positive rate to about .12, which is less than half that of the next-best defense. We also demonstrate DeTorrent's practicality by deploying it alongside the Tor network and find that it maintains its performance when applied to live traffic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
