关键词: 深度学习, 加密流量, 网站指纹识别, 洋葱网络, 虚拟私人网络

Abstract: Website fingerprinting is an important research area within the fields of network security and privacy protection. Its goal is to identify websites accessed by users within an encrypted network environment by analyzing network traffic characteristics. In response to the problems of limited application scenarios, such as restricted application scenarios, insufficient applicability, and the singularity of feature selection, this paper proposes a deep learningbased method for encrypted website fingerprinting. Initially, a new preprocessing method for raw data packets is introduced, which processes directly captured raw packet files to generate a feature sequence with both spatial and temporal characteristics, structured hierarchically. Following this, a hybrid deep learning model combining convolutional neural networks and long shortterm memory networks is designed to thoroughly learn the spatial and temporal features present in the data. The study further investigates various activation functions, model parameters, and optimization algorithms to improve the model’s accuracy and generalization capability. Experimental results indicate that this method provides higher website fingerprinting accuracy in the onion router anonymous network environment when it does not rely on cell packets. And it also achieves better accuracy compared to current mainstream machine learning methods in virtual private network scenarios.

Key words: deep learning, encrypted traffic, website fingerprinting, the onion router, virtual private network