ELFA-Log: Cross-System Log Anomaly Detection via Enhanced Pseudo-Labeling and Feature Alignment - Xiaowei Zhao; Kaiwei Guo; Mingting Huang; Shaojian Qiu; Lu Lu (Computers)
Existing log-based anomaly detection methods typically require large volumes of labeled data for training, presenting significant challenges when applied to new systems with limited labeled data. This limitation has spurred the need for cross-system log anomaly detection (CSLAD) methods. However, current CSLAD approaches often face challenges in effectively handling distributional differences in log data across systems. To address this issue, we propose ELFA-Log, a transfer learning-based approach for cross-system log anomaly detection. By enhancing pseudo-label generation with uncertainty estimation and feature alignment, ELFA-Log improves detection performance even in the presence of data distribution shifts. It uses entropy-based metrics to generate high-confidence pseudo-labels, minimizing reliance on labeled data. Additionally, a distance-based loss function optimizes the shared representation of cross-system log features. Experimental results on benchmark datasets demonstrate that ELFA-Log enhances the performance of CSLAD, offering a practical solution to the challenge of high labeling costs in real-world applications.