Escape method of malicious traffic based on backdoor attack - MA Bowen; GUO Yuanbo; MA Jun; ZHANG Qi; FANG Chen (Tongxin xuebao)
Launching backdoor attacks against deep learning (DL)-based network traffic classifiers, and a method of malicious traffic escape was proposed based on the backdoor attack. Backdoors were embedded in classifiers by mixing poisoned training samples with clean samples during the training process. These backdoor classifiers then identified the malicious 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 triggers 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 effectiveness of the proposed method was verified through experiments, providing a new approach for escaping malicious traffic from classifiers.