MTDM-MS: A Malicious Traffic Detection Model Based on Multi-Category Signals

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

Abstract: The demand for malicious traffic detection continues to rise along with the development of the Internet. Existing methods perform with flaws in complex feature extraction processes and interference by obfuscation techniques and other means. In addition, the performance is unstable in new scenarios, and the generalization ability is not good enough. Therefore, this paper proposes a malicious traffic detection model based on multi-category signals (MTDM-MS), which adopts multiple modules to extract the features of text sequence signals and image signals of the traffic, respectively, to realize the interaction of various feature information and improve the model's characterization ability. Ablation experiments verify the effectiveness of each module. Experimental results in several datasets show that MTDM-MS possesses considerable detection performance and generalization ability with a 2.2% to 8.4% improvement in macro-F1 compared with the control models.