| With the increasing awareness of information security and user privacy protection,more and more social software develops and uses private encryption protocols and widely adopts end-to-end encryption technology,which leads to the disorderly spread of some illegal activities through social networks.As a result,the regulatory departments cannot effectively supervise social networks,so how effectively identify the user behavior of social software is an urgent problem with high research value.Machine learning or deep learning methods based on flow statistical features have achieved specific achievements in social software user behavior identification.Still,these methods are susceptible to interference data in traffic data and fluctuation of traffic features.At the same time,the diversity of social software user behaviors and the complexity of these behaviors triggering traffic further lead to poor social software identification performance and high resource consumption,which are not suitable for scenarios with fine granularity and high real-time requirements.The text proposes a new user behavior identification method for social software to address the above problems,which can accomplish real-time and fine-grained user behavior identification of social software.The main research contents of this paper are as follows.(1)A method for identifying user behavior of social software based on service data matrix is proposed.Most of the existing studies extract features from the total traffic generated by social software,leading to susceptibility to interference data.This paper attempts to filter the traffic data that better portrays user behavior from the total traffic and proposes the concept of control service and a control service extraction method based on the service data matrix.Based on the Whats App control service features,two neural network structures-LS-CNN and LSLSTM-are designed to extract features from packet load length sequences of control services to identify user behaviors.Using this method to identify 14 user behaviors of Whats App,experiments demonstrate that the technique has a more significant improvement in granularity,stability,and overall identification accuracy than existing methods.(2)A real-time identification method for continuous user behavior is proposed.This paper uses a burst threshold-based approach to segment continuous user behaviors and obtain the control services corresponding to each sub behavior to identify continuous user behaviors in real-time.Control services with different continuous user behaviors of sub behaviors have high similarity,so they cannot be placed directly using LS-CNN.In this paper,we propose a twolayer classification model based on LS-CNN to distinguish which continuous user behavior a sub behavior belongs to based on the time dependence of the continuous user behavior.The main classifier in the first layer of the model identifies all samples triggered by user behaviors,and selects samples triggered by continuous user behaviors based on the identification results.Then sends them to the second layer classifier to complete the fine-grained identification of continuous user behaviors.The experimental results show that the model can identify continuous user behaviors in real-time and at fine granularity.(3)In this paper,we design and implement a real-time Whats App user behavior identification system based on the above method.The system mainly contains a real-time traffic collection module,traffic processing module,real-time user behavior identification module and interface display module.The system has a simple interface that allows users to run the system with simple parameter settings and displays the current behavior of Whats App users in the network through the interface.In addition,this paper uses the replayed traffic data to simulate Whats App’s real-time traffic to test the system’s performance.Through the test,the system operates stably,and the identification accuracy and real-time performance meet the expectation. |
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