Neural-FacTOR: Neural Representation Learning for Website Fingerprinting Attack over TOR Anonymity
This work addresses the challenge of monitoring online crimes by enhancing de-anonymization in TOR networks, representing an incremental improvement over existing methods.
The paper tackled the problem of website fingerprinting attacks over TOR networks by proposing a neural representation learning approach using a CNN with dilation and causal convolution, which improved accuracy by 12.21% compared to state-of-the-art methods on three public datasets.
TOR (The Onion Router) network is a widely used open source anonymous communication tool, the abuse of TOR makes it difficult to monitor the proliferation of online crimes such as to access criminal websites. Most existing approches for TOR network de-anonymization heavily rely on manually extracted features resulting in time consuming and poor performance. To tackle the shortcomings, this paper proposes a neural representation learning approach to recognize website fingerprint based on classification algorithm. We constructed a new website fingerprinting attack model based on convolutional neural network (CNN) with dilation and causal convolution, which can improve the perception field of CNN as well as capture the sequential characteristic of input data. Experiments on three mainstream public datasets show that the proposed model is robust and effective for the website fingerprint classification and improves the accuracy by 12.21% compared with the state-of-the-art methods.