Abstract: In the domain of traffic detection,the challenge of defending against adversarial examples is significant.Traditional adversarial example defense methods,which rely heavily on adversarial training,necessitate a vast quantity of adversarial examples for training purposes.However,a notable drawback of such approaches is the resultant decrease in the recognition accuracy of the original,unaltered data.This reduction in accuracy poses a substantial problem,as it compromises the effectiveness of the defense mechanism in recognizing legitimate traffic patterns.To address these challenges,a novel approach to traffic adversarial example defense has been proposed,leveraging the concept of feature transfer.This innovative method ingeniously combines two strategic defense philosophies:firstly,enhancing the robustness of the model against adversarial attacks,and secondly,obfuscating the space within which adversarial examples operate.The defense mechanism is architecturally composed of two integral modules:a lower-level defense module equipped with denoising capabilities,and a recognition module designed for the explicit purpose of identifying traffic patterns.The cornerstone of this approach is the employment of a stacked autoencoder as the foundational element of the lower-level defense module.This choice is pivotal,as the autoencoder excels in adversarial knowledge learning,thereby endowing the system with the capability to extract and understand adversarial features effectively.This is a critical step in ensuring that the defense mechanism can preemptively neutralize potential adversarial threats.Subsequently,the system embarks on a phase of functional adaptation,tailored specifically to the characteristics of network traffic.This phase involves the construction of adaptive functionalities based on the distinct features of traffic,followed by the training of the recognition module using non-adversarial traffic data.This strategic training empowers the recognition module with the ability to accurately identify legitimate traffic patterns,thereby significantly enhancing the overall efficacy of the defense mechanism.A key innovation of this method is the conceptual separation of defense and recognition functionalities.This separation not only reduces the operational costs asso-ciated with defense but also minimizes the adverse impact of adversarial training on the recognition accuracy of original data.As a result,the system achieves a rapid adaptation to evolving threats,significantly improving the model's defensive resilience.Empirical evidence supports the effectiveness of this approach,with the recognition accuracy for new adversarial examples experiencing a substantial increase to approximately 40%.This improvement marks a significant advancement in the field of traffic detection and adversarial example defense,offering a promising avenue for future research and development.

Key words: Intrusion detection, Traffic adversarial example, Adversarial example defense, Defensive knowledge transfer