A robust Wide & Deep learning framework for log-based anomaly detection

Log -based anomaly detections have shown huge commercial potential in system maintenance. However, existing methods encounter two practical challenges. Firstly, they struggle to maintain consistent performance when dealing with evolving logs over time. Secondly, they face difficulties in effectively detecting frequency anomalies, such as abnormal system resource usage and abnormal system operating frequencies. In this paper, we propose a robust log -based anomaly detection framework using Wide & Deep learning called WDLog. Particularly, we enhance the processing of template semantic information by building upon the Drain algorithm, then we introduce invariant features and statistical features and propose a multi -feature anomaly detection method based on the Wide & Deep framework. The experimental results on HDFS and BGL datasets demonstrate the promising performance of WDLog compared to state-of-the-art methods in terms of anomaly detection effectiveness. Furthermore, WDLog exhibits robustness to evolving logs, achieving F1 -scores of over 90% under different degrees of log variation.