基于后门攻击的恶意流量逃逸方法OA北大核心CSTPCD

Launching backdoor attacks against deep learning(DL)-based network traffic classifiers,and a method of ma-licious traffic escape was proposed based on the backdoor attack.Backdoors were embedded in classifiers by mixing poi-soned training samples with clean samples during the training process.These backdoor classifiers then identified the ma-licious traffic with an attacker-specific backdoor trigger as benign,allowing the malicious traffic to escape.Additionally,backdoor classifiers behaved normally on clean samples,ensuring the backdoor's concealment.Different backdoor trig-gers were adopted to generate various backdoor models,the effects of different malicious traffic on different backdoor models were compared,and the influence of different backdoors on the model's performance was analyzed.The effective-ness of the proposed method was verified through experiments,providing a new approach for escaping malicious traffic from classifiers.