Large Language Models Powered Malicious Traffic Detection: Architecture, Opportunities, and Case Study

Xinggong Zhang, Haotian Meng, Qingyang Li, Yunpeng Tan, Lei Zhang

Abstract: Malicious traffic detection is a pivotal technology for network security to identify abnormal network traffic and detect network attacks. Large Language Models (LLMs) are trained on a vast corpus of text, have amassed remarkable capabilities of context-understanding and commonsense knowledge. This has opened up a new door for network attacks detection. Researchers have already initiated discussions regarding the application of LLMs on specific cyber-security tasks. Unfortunately, there remains a lack of comprehensive analysis on harnessing LLMs for traffic detection, as well as the opportunities and challenges. In this paper, we focus on unleashing the full potential of Large Language Models (LLMs) in malicious traffic detection. We present a holistic view of the architecture of LLM-powered malicious traffic detection, including the procedures of Pre-training, Fine-tuning, and Detection. Especially, by exploring the knowledge and capabilities of LLM, we identify three distinct roles LLM can act in traffic classification: Classifier, Encoder, and Predictor. For each of them, the modeling paradigm, opportunities and challenges are elaborated. Finally, we present our design on LLM-powered DDoS detection as a case study. The proposed framework attains accurate detection on carpet bombing DDoS by exploiting LLMs’ capabilities in contextual mining. The evaluation shows its efficacy, exhibiting a nearly 35% improvement compared to existing systems.

External IDs:dblp:journals/network/ZhangMLTZ25