【摘要】 网站指纹识别技术是网络安全和隐私保护领域的一个重要研究方向，其目标是通过分析网络流量特征识别出用户在加密的网络环境中访问的网站．针对目前主流方法存在应用场景有限、适用性不足以及特征选取单一等问题，提出了一种基于深度学习的加密网站指纹识别方法．首先，设计了一种新的原始数据包的预处理方法，可以基于直接抓包得到的原始数据包文件得到一个包含空间和时间双特征的具备层次结构的特征序列．然后，设计了一种基于卷积神经网络和长短期记忆网络的融合深度学习模型，充分学习数据中包含的空间和时间特征．在此基础上，进一步探索了不同的激活函数、模型参数和优化算法，以提高模型的识别准确率和泛化能力．实验结果表明，在洋葱匿名网络环境下不依赖其数据单元（cell）时，可展现出更高的网站指纹识别准确率，同时在虚拟私人网络场景下也取得了相较于目前主流机器学习方法更高的准确率．更多还原

【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.更多还原