MLMTD: A Multi-Layer Malicious Traffic Detection Model Based on Multi-Branch Octave Convolution and Attention Mechanism

Ziang Li, Chengxiang Si, Zhenyu Cheng, Shuyuan Zhao, Yong Ding

Abstract: Malicious traffic detection is important for the safe operation of cyberspace. Existing methods are difficult to extract discriminative features, leading to the detection rate bottleneck. In addition, the performance is significantly degraded in sample imbalanced scenarios, with poor generalization ability and insufficient scalability. Therefore, this paper proposes a multi-layer malicious traffic detection model based on multi-branch octave convolution and attention mechanism (MLMTD), which adopts multiple modules to extract more diverse feature information and learn important features more adequately in multiple dimensions. Ablation experiments validate the effectiveness of each module. The results in several experimental scenarios show that MLMTD can achieve better results with fewer features and attain superior robustness and generalization ability compared to the control models.