Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study

TL;DR

This paper investigates how reducing log data affects anomaly detection models, introduces LogCleaner to optimize logs, and demonstrates significant improvements in speed and accuracy across multiple datasets.

Contribution

The study provides the first comprehensive analysis of log reduction effects on anomaly detection and proposes LogCleaner, an effective method for automatic log event filtering.

Findings

LogCleaner reduces over 70% of log events

Inference speed increases by approximately 300%

Model performance in anomaly detection is universally improved

Abstract

As software systems grow increasingly intricate, the precise detection of anomalies have become both essential and challenging. Current log-based anomaly detection methods depend heavily on vast amounts of log data leading to inefficient inference and potential misguidance by noise logs. However, the quantitative effects of log reduction on the effectiveness of anomaly detection remain unexplored. Therefore, we first conduct a comprehensive study on six distinct models spanning three datasets. Through the study, the impact of log quantity and their effectiveness in representing anomalies is qualifies, uncovering three distinctive log event types that differently influence model performance. Drawing from these insights, we propose LogCleaner: an efficient methodology for the automatic reduction of log events in the context of anomaly detection. Serving as middleware between software systems and models, LogCleaner continuously updates and filters anti-events and duplicative-events in the raw generated logs. Experimental outcomes highlight LogCleaner's capability to reduce over 70% of log events in anomaly detection, accelerating the model's inference speed by approximately 300%, and universally improving the performance of models for anomaly detection.