Traffic2Chain: Revealing Covert Multi-Step Attacks Through Unsupervised Traffic Behaviour Correlation
Jiang Xie, Shuhao Li, Xiaochun Yun, Tao Yin, Hongbo Xu, Peishuai Sun · IEEE Transactions on Information Forensics and Security · 2025
With the continuous development of network technology, covert multi-step attacks have become one of the significant attack methods. It is a multi-step attack with the intention of destroying the system- or data-privacy, such as network stealing. Current methods usually generate single-step alerts first and then perform correlation analysis. However, it is difficult for these methods to perform fine-grained annotation and alert amount control for single-step alerts, as well as to completely correlate the alerts of different phases into a chain due to alert fatigue. In this paper, we propose Traffic2Chain, an innovative unsupervised traffic behaviour correlation method to detect covert multi-step attacks from the network side. Traffic2Chain (1) generates alerts at different phases in real-time and annotates to sub-techniques based on MITRE ATT&CK knowledge database; (2) performs alert clustering based on SIMCSE and automatically generates event descriptions based on the Large Language Model (LLM) technique, and (3) extracts the attack chain through multi-dimensional information correlation to reveal the complete attack process. Experimental results demonstrate that the F1 score of Traffic2Chain reaches 98.36%, which has a significant advantage over other methods. In the real-world network, the detection speed can reach 40 Gbps. Most importantly, we discovered an unknown attack pattern based on Traffic2Chain - attackers delivered a variant of the Silver Fox Trojan by impersonating VPN services, eventually building a botnet with stealing capabilities and a node size of more than one million.