Jizhao Liu, Yuanyuan Shao*, Shuqin Zhang, Fangfang Shan, Jun Li
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.068998
- 10 November 2025
Abstract Lateral movement represents the most covert and critical phase of Advanced Persistent Threats (APTs), and its detection still faces two primary challenges: sample scarcity and “cold start” of new entities. To address these challenges, we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework (UGEA-LMD). First, the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution, enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem. Second, in the embedding space, we model the dependency structure among… More >
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