Feedback-Aware Anomaly Detection Through Logs for Large-Scale Software Systems

One particular challenge for large-scale software systems is anomaly detection.System logs are a straightforward and common source of information for anomaly detection.Existing log-based anomaly detectors are unusable in real-world industrial systems due to high false-positive rates.In this paper,we incorporate human feedback to adjust the detection model structure to reduce false positives.We apply our approach to two industrial large-scale systems.Results have shown that our approach performs much better than state-of-the-art works with 50% higher accuracy.Besides,human feedback can reduce more than 70% of false positives and greatly improve detection precision.