InterpretableSAD: Interpretable Anomaly Detection in Sequential Log Data

Abstract

We propose InterpretableSAD, a framework for anomaly detection in sequential log data with built-in interpretability. The model detects abnormal subsequences while providing explanations through interpretable prototypes. Experiments on system log datasets demonstrate that InterpretableSAD achieves competitive detection accuracy while offering human-understandable explanations.