[2511.05878] FusionLog: Cross-System Log-based Anomaly Detection via Fusion of General and Proprietary Knowledge

Computer Science > Machine Learning arXiv:2511.05878 (cs) [Submitted on 8 Nov 2025 (v1), last revised 26 Mar 2026 (this version, v3)] Title:FusionLog: Cross-System Log-based Anomaly Detection via Fusion of General and Proprietary Knowledge Authors:Xinlong Zhao, Tong Jia, Minghua He, Xixuan Yang, Ying Li View a PDF of the paper titled FusionLog: Cross-System Log-based Anomaly Detection via Fusion of General and Proprietary Knowledge, by Xinlong Zhao and 3 other authors View PDF HTML (experimental) Abstract:Log-based anomaly detection is critical for ensuring the stability and reliability of web systems. One of the key problems in this task is the lack of sufficient labeled logs, which limits the rapid deployment in new systems. Existing works usually leverage large-scale labeled logs from a mature web system and a small amount of labeled logs from a new system, using transfer learning to extract and generalize general knowledge across both domains. However, these methods focus solely on the transfer of general knowledge and neglect the disparity and potential mismatch between such knowledge and the proprietary knowledge of target system, thus constraining performance. To address this limitation, we propose FusionLog, a novel zero-label cross-system log-based anomaly detection method that effectively achieves the fusion of general and proprietary knowledge, enabling cross-system generalization without any labeled target logs. Specifically, we first design a training-free rou...