MUFFLER: Secure Tor Traffic Obfuscation with Dynamic Connection Shuffling and Splitting
Minjae Seo, Myoungsung You, Jaehan Kim, Taejune Park, Seungwon Shin, Jinwoo Kim
摘要
Tor, a widely utilized privacy network, enables anonymous communication but is vulnerable to flow correlation attacks that deanonymize users by correlating traffic patterns from Tor's ingress and egress segments. Various defenses have been developed to mitigate these attacks; however, they have two critical limitations: (i) significant network overhead during obfuscation and (ii) a lack of dynamic obfuscation for egress segments, exposing traffic patterns to adversaries. In response, we introduce MUFFLER, a novel connection-level traffic obfuscation system designed to secure Tor egress traffic. It dynamically maps real connections to a distinct set of virtual connections between the final Tor nodes and targeted services, either public or hidden. This approach creates egress traffic patterns fundamentally different from those at ingress segments without adding intentional padding bytes or timing delays. The mapping of real and virtual connections is adjusted in real-time based on ongoing network conditions, thwarting adversaries' efforts to detect egress traffic patterns. Extensive evaluations show that MUFFLER mitigates powerful correlation attacks with a TPR of 1% at an FPR of 10 -2 while imposing only a 2.17% bandwidth overhead. Moreover, it achieves up to 27x lower latency overhead than existing solutions and seamlessly integrates with the current Tor architecture.
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它引用的顶会 Paper13
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- Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial PerturbationsMilad Nasr, Alireza Bahramali, Amir HoumansadrUSENIX Security 2021 · 被引用 142 次
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- Inside Job: Applying Traffic Analysis to Measure Tor from WithinRob Jansen, Marc Juarez, Rafa Gálvez, Tariq Elahi 等NDSS 2018 · 被引用 86 次
- SPRIGHT: extracting the server from serverless computing! high-performance eBPF-based event-driven, shared-memory processingShixiong Qi, Leslie Monis, Ziteng Zeng, Ian-Chin Wang 等SIGCOMM 2022 · 被引用 85 次
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