The development of smartphones and social networks has brought great convenience to our lives. Due to the increasing requirements of user privacy, user data are protected by encryption protocol. Nevertheless, the encrypted traffic may still be identificated by a third party. In order to improve the privacy protection of users, it is necessary to study the existing encrypted user behavior system. The existing user behavior identification adopts the statistical features of encrypted traffic, which fluctuates greatly in different transmission environments. In this paper, we propose a Stable Features Identification Method(SFIM), which concentrate on filtering out the stable features from the encrypted traffic to identify user behavior. Based on the principle of maximum entropy, we put forward an approach to divide the distribution ranges of these stable features, and map the feature space into vector space. Our research focuses on multiple user behavior in the Instagram application. The best evaluation results achieve 99.8% accuracy, 99.3% precision, 99.3% recall, and 0.09% false positive rate(FPR) on average.

智能手机和社交网络的发展给我们的生活带来了极大的便利。由于对用户隐私的要求越来越高，用户数据受到加密协议的保护。尽管如此，加密流量仍可能被第三方识别。为了提高对用户的隐私保护，有必要对现有的加密用户行为系统进行研究。现有的用户行为识别采用加密流量的统计特征，在不同的传输环境下波动较大。在本文中，我们提出了一种稳定特征识别方法（SFIM），该方法专注于从加密流量中过滤掉稳定特征以识别用户行为。根据最大熵原理，我们提出了一种方法来划分这些稳定特征的分布范围，并将特征空间映射到向量空间。我们的研究侧重于 Instagram 应用程序中的多个用户行为。最佳评估结果平均达到 99.8% 的准确率、99.3% 的准确率、99.3% 的召回率和 0.09% 的误报率（FPR）。