关键词: 网站指纹攻击, 匿名通信, 流量分析, 卷积神经网络, SSH协议

Abstract: SSH anonymous communication system is an effective means to protect information security and user’s privacy, but SSH anonymous communication may also be used for cybercrime activities. For the problem of SSH anonymous communication system being abuse is difficult to supervise, a website fingerprinting attack method against SSH anonymous traffic based on one-dimensional convolution neural network is proposed. The method integrates the feature engineering and the classification prediction step, and avoids the manual process of feature extraction, selection and combination in the traditional fingerprinting attack method. The method converts the downlink network stream into corresponding numeric values and normalizes them, then extracts the high-dimensional trafficfingerprint features and predicts the classification using the depth one-dimensional convolutional neural network. Through the experiment, using fingerprinting attack on 100 website targets, the accuracy rate reaches 92.03% and it indicates that the method can effectively attack the SSH anonymous communication.

Key words: website fingerprinting attack, anonymous communication, traffic analysis, convolutional neural network, SSH protocol