SMART: A Lightweight and Reliable Multi-Path Transmission Model against Website Fingerprinting Attacks

Abstract

The rapid development of IoT technology has promoted the integration of physical space and cyberspace. At the same time, it has also increased the risk of privacy leakage of Internet users. A large number of research works have shown that attackers can infer Internet surfing privacy through traffic patterns without decryption. Most of the existing research work on anti-traffic analysis is based on a weakened experimental assumption, which is difficult to apply in the actual IoT network environment and seriously affects the user experience. This article proposes a novel lightweight and reliable defense—SMART, which can ensure the anonymity and security of network communication without sacrificing network transmission performance. SMART introduces a multi-path transmission model in the Tor network, and divides traffic at multiple Tor entry onion relays, preventing attackers from obtaining network traffic statistical characteristics. We theoretically proved that SMART can improve the uncertainty of website fingerprint analysis results. The experimental result shows that SMART is able to resist encrypted traffic analysis tools, reducing the accuracy of four state-of-the-art classifiers from 98% to less than 12%, without inducing any additional artificial delay or dummy traffic. In order to avoid the performance degradation caused by data reassembly, SMART proposes a redundant slice mechanism to ensure reliability. Even in the case of human interference, the communication success rate is still as high as 97%.