CAM-LDS: cyber attack manifestations for automatic interpretation of system logs and security alerts

Abstract Log data are essential for intrusion detection and forensic investigations. However, manual log analysis is tedious due to high data volumes, heterogeneous event formats, and unstructured messages. Although many automated log-analysis methods exist, they usually still rely on domain-specific configurations, including expert-defined detection rules, handcrafted log parsers, or manual feature-engineering. Crucially, the level of automation of conventional methods remains limited by their inability to semantically understand logs and explain their underlying causes. In contrast, Large Language Models enable domain- and format-agnostic interpretation of system logs and security alerts. Unfortunately, research on this topic is challenging, because publicly available labeled data sets covering a broad range of attack techniques remain scarce. To address this gap, we introduce the Cyber Attack Manifestation Log Data Set (CAM-LDS), a fully open-source and reproducible Linux-based data set for analyzing cyber attack manifestations in system logs and security alerts. Unlike existing datasets that are often network-centric, Windows-focused, or limited in attack diversity, CAM-LDS comprises seven attack scenarios covering 81 techniques across 13 tactics and collected from 18 host- and network-based data sources. We isolate log events temporally associated with individual attack steps to enable analysis of manifestations concerning command observability, event frequencies, performance metrics, and intrusion detection alerts. We further present an illustrative case study utilizing an LLM to process CAM-LDS. The best-performing model ranks at least one correct attack technique first for $$41.8\%$$ 41.8 % of attack steps and within Top 10 for $$67.2\%$$ 67.2 % , demonstrating the potential of LLM-based log interpretation and utility of our data set.