Log-based Anomaly Detection with Deep Learning: How Far Are We?
This work highlights critical evaluation gaps in a domain-specific area (software systems) for researchers and practitioners, showing that current methods are not robust and the problem is incremental.
The paper analyzed five deep learning models for log-based anomaly detection across four public datasets, finding that evaluation aspects like training data selection and class distribution significantly impact performance, and that the problem remains unsolved despite high reported accuracy.
Software-intensive systems produce logs for troubleshooting purposes. Recently, many deep learning models have been proposed to automatically detect system anomalies based on log data. These models typically claim very high detection accuracy. For example, most models report an F-measure greater than 0.9 on the commonly-used HDFS dataset. To achieve a profound understanding of how far we are from solving the problem of log-based anomaly detection, in this paper, we conduct an in-depth analysis of five state-of-the-art deep learning-based models for detecting system anomalies on four public log datasets. Our experiments focus on several aspects of model evaluation, including training data selection, data grouping, class distribution, data noise, and early detection ability. Our results point out that all these aspects have significant impact on the evaluation, and that all the studied models do not always work well. The problem of log-based anomaly detection has not been solved yet. Based on our findings, we also suggest possible future work.