PathWatcher: A path-based behavior detection method for attack detection and investigation
Zehui Wang, Hao Li, Yinhao Qi, Wei Qiao, Song Liu, Chen Zhang, Bo Jiang, Zhigang Lu · Computers & Security · 2025
Advanced Persistent Threats (APTs) comprise complex and stealthy attack techniques. Due to the characteristics of system audit logs in capturing system-level process calls and providing granular log data, using audit logs for causal analysis of advanced threat behaviors has become a popular solution. However, existing solutions still suffer from several deficiencies: (1) semantic gaps between raw data in low-level views and high-level system behaviors, (2) fatigue alert, and (3) poor interpretability and inferability. In this paper, we propose PathWatcher, a path-based behavior detection method, which enables attack investigation based on detection results. PathWatcher enhances low-level semantics by combining operation sequences, extracting paths as behavioral entities from the provenance graph, and learning path features. This approach reduces the semantic gap between low-level data and high-level system behaviors. PathWatcher first performs graph construction and path extraction in the graph construction module, followed by feature learning of nodes and paths in the behavioral sequence extraction module, the data generated during the process exists in the path record with a certain rule, and finally the data from the path record is used for feature extraction and path tracing in the behavior identification and attack clues module, the data from the path record is used for feature extraction and path tracing. This model exhibits strong inferability and interpretability by matching paths to operational behaviors in logs. This allows security researchers to combine path records and investigate attacks directly using high-level semantics, thereby alleviating alert fatigue. Our experimental results demonstrate that PathWatcher effectively improves the detection accuracy of malicious behaviors while enhancing semantic interpretability. The detection results are inferable, achieving accuracies of 99.76% and 99.07% on two datasets, and we provide an analysis of attack investigations.