Affiliation:
1. Hong Kong University of Science and Technology
2. University of Waterloo
Abstract
Abstract
Website fingerprinting allows a local, passive observer monitoring a web-browsing client’s encrypted channel to determine her web activity. Previous attacks have shown that website fingerprinting could be a threat to anonymity networks such as Tor under laboratory conditions. However, there are significant differences between laboratory conditions and realistic conditions. First, in laboratory tests we collect the training data set together with the testing data set, so the training data set is fresh, but an attacker may not be able to maintain a fresh data set. Second, laboratory packet sequences correspond to a single page each, but for realistic packet sequences the split between pages is not obvious. Third, packet sequences may include background noise from other types of web traffic. These differences adversely affect website fingerprinting under realistic conditions. In this paper, we tackle these three problems to bridge the gap between laboratory and realistic conditions for website fingerprinting. We show that we can maintain a fresh training set with minimal resources. We demonstrate several classification-based techniques that allow us to split full packet sequences effectively into sequences corresponding to a single page each. We describe several new algorithms for tackling background noise. With our techniques, we are able to build the first website fingerprinting system that can operate directly on packet sequences collected in the wild.
Cited by
100 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A website fingerprinting technology with time-sampling;Peer-to-Peer Networking and Applications;2024-02-06
2. Joint Alignment Networks For Few-Shot Website Fingerprinting Attack;The Computer Journal;2024-02-02
3. A Multi-tab Webpage Fingerprinting Method Based on Multi-head Self-attention;Communications in Computer and Information Science;2024
4. Realistic Website Fingerprinting By Augmenting Network Traces;Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security;2023-11-15
5. A Critical Study of Few-Shot Learning for Encrypted Traffic Classification;2023 19th International Conference on Network and Service Management (CNSM);2023-10-30