Research on malicious encrypted traffic detection method based on Laplacian image sharpening

With the intensification of the international situation and continuous regional conflicts, modern warfare has shifted from a human wave tactic to today’s information warfare, and the situation of network security has become increasingly severe. In order to effectively improve network security defense capabilities and address the current problem of rampant malicious encrypted traffic and the inability of traditional detection methods to effectively detect and intercept it, this paper proposes a malicious encrypted traffic detection method based on the characteristics of malicious encrypted traffic. By introducing the fusion Laplace image sharpening operator to sharpen the characteristics of malicious encrypted traffic, the accuracy of model detection is further improved. The feasibility of this method is fully demonstrated through experiments on the CTU-13 public dataset, with an accuracy rate of 98.06%, which is significantly improved compared to other similar models. This provides a new approach for subsequent research on malicious encrypted traffic detection.