基于深度学习的多会话协同攻击加密流量检测技术研究OA北大核心

Malicious encrypted traffic detection is currently an important research topic in the field of network security.Attacker used multi-session encrypted traffic to achieve multi-stage coordinated attacks,which is becoming a trend.This paper analyzes the existing problems of current mainstream malicious encrypted traffic detection methods,and proposes an malicious encrypted traffic detection method for multi-session coordinated attack scenarios.Based on the advantages of deep learning methods in the field of image recognition,this method extracts multi-session features and converts them into images,converting encrypted traffic identification problems into image recognition problems,thereby indirectly realizes malicious encrypted traffic detection.The preliminary test results on the experimental data have verified the effectiveness of the method.