Instagram User Behavior Identification Based on Multidimensional Features

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. But it also makes it difficult to regulate malicious behavior. 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 method to obtain the stable features of encrypted traffic. 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 SVM vector space. Our research focuses on multiple user behavior in the Instagram application. The evaluation results achieve 99.8% accuracy, 99.3% precision, 99.3% recall, and 0.09% false positive rate(FPR) on average.