Zero in on the Target: A Composite Robust Model for Retrieving Information in Traffic Data to Discover Network Attacks

摘要

With the popularization of the Internet and the diversification of attack methods, web security has become an important part of information security. As a carrier of network behavior, traffic can reveal attack behaviors in the Web environment through malicious traffic detection. Since images can fully express spatial features and local associations, it is feasible to visualize traffic as images and retrieve key feature information to detect malicious traffic. However, existing methods are prone to redundancy during feature extraction. Secondly, a single perspective makes it difficult to learn patterns with universality from diverse feature information. In addition, the selection of segmentation thresholds in the preprocessing is closely related to the model's information retrieval effect. The commonly adopted preset thresholds are difficult to cope with the changes in traffic data, limiting the applicability of the existing methods. Therefore, this paper proposes a Multi-Module-Based Composite Robust Model for Network Attack Detection (MCNAD). The model adopts depthwise separable convolution (DSC) to reduce redundant information, proposes a multi-scale feature learning module to enhance the model characterization ability, and proposes a gray level co-occurrence matrix segmentation algorithm with adaptive threshold (GLCM-AT) to optimize data preprocessing. The results show that MCNAD improves detection performance with better detection efficiency, generalization ability, and robustness, demonstrating its wide applicability in multiple scenarios.