LM-Hunter: An NLP-powered graph method for detecting adversary lateral movements in APT cyber-attacks at scale

Abstract

APT (Advanced Persistent Threat) actors are highly skilled cyber attackers who employ sophisticated techniques to infiltrate and maintain unauthorized access to a network over an extended period. In the APT lifecycle, lateral movement stands out as a critical stage where intruders escalate privileges and move across the network to expand their control and access to sensitive data. While solutions such as UEBA (User and Entity Behavior Analytics) or graph analysis have been proposed to identify lateral movements, their application in real-world cybersecurity incidents remains impractical in terms of both scalability and performance. This paper introduces LM-Hunter, a new robust and efficient method for identifying stealth adversaries moving laterally through the network at scale. LM-Hunter takes advantage of graphs and Transformers, a specific architecture within NLP (Natural Language Processing), to learn the network dynamics for hunting the most suspicious lateral movements of the users. The method is validated in a real-world cybersecurity incident at a Fortune 500 company, one of the largest corporations in the United States, demonstrating its capability to identify adversarial lateral movements in large enterprise networks. LM-Hunter enhances the threat detection capabilities of Incident Response and Threat Hunting teams in real-world scenarios. The application of the method is facilitated by releasing LM-Hunter as an open-source tool, expanding the arsenal of cybersecurity teams for combating cyber threats.