Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study
This work addresses the challenge of handling large, noisy log data for anomaly detection in software systems, offering a practical middleware solution with significant efficiency gains.
The study tackled the problem of inefficient and noisy log-based anomaly detection by empirically analyzing the impact of log quantity on model performance, leading to the development of LogCleaner, which reduces log events by over 70%, speeds up inference by approximately 300%, and improves detection performance.
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.