基于后门攻击的恶意流量逃逸方法

Escape method of malicious traffic based on backdoor attack

针对基于深度学习模型的流量分类器,提出了一种利用后门攻击实现恶意流量逃逸的方法。通过在训练过程添加毒化数据将后门植入模型,后门模型将带有后门触发器的恶意流量判定为良性,从而实现恶意流量逃逸;同时对不含触发器的干净流量正常判定,保证了模型后门的隐蔽性。采用多种触发器分别生成不同后门模型,比较了多种恶意流量对不同后门模型的逃逸效果,同时分析了不同后门对模型性能的影响。实验验证了所提方法的有效性,为恶意流量逃逸提供了新的思路。

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.