Action Identification Without Bounds on Applications Based on Self-Attention Mechanism

In recent years, the industrial Internet has experienced a rapid development. However, like the traditional Internet, the industrial Internet also faces a large number of threats from cyber-attacks and sensitive information leakage risks. Traffic classification technology, especially fine-grained application action identification, can assist network managers in detecting abnormal behaviors and discovering privacy leakage risks. It provides the security of the industrial Internet. Whereas, the existing action identification technology relies on the pre-segmentation of the action bounds in the traffic. In this case, existing methods cannot identify actions without bounds, which are difficult to be used in real scenes. Therefore, an action identification algorithm without bounds is proposed. Firstly, we build a packet-level identification model based on self-attention mechanism to classify packets. Then we propose an action aggregation algorithm to acquire action sequence from the classification results of packets. Finally, we establish two new indicators to measure the quality of the identification result. To verify the feasibility of our algorithm, we take WeChat as an example to conduct experiments. The results show that the model can achieve a sequential precision of up to 90%. This research is expected to greatly promote the practical application of action identification technology.