TOR Anonymous Traffic Fingerprint Extraction and Recognition Based on Neural Network

Abstract

The TOR anonymous communication system is an important means to protect network communication security and user privacy, but there are still criminals trying to destroy the confidentiality of the anonymous communication system through some special methods. Aiming at the abuse of the TOR anonymous communication system, this paper proposes a neural network-based anonymous traffic identification method, which uses a one-dimensional convolutional neural network for feature extraction, prediction and classification, and finally integration. In the experiment, nearly 100 websites were selected for the flow feature extraction and recognition based on one-dimensional convolutional neural network. The recognition accuracy rate is 87.5%, indicating that this method can effectively fingerprint TOR anonymous communication.