Abstract:
"Log anomaly detection and root cause analysis play a crucial role in the optimization of the
DevOps lifecycle. However, existing methods struggle to efficiently handle the large volume and
diverse formats of log data, resulting in delayed detection and diagnosis of issues.
This research proposes a novel machine learning-based approach for efficient log anomaly
detection and root cause analysis. The model is designed to extract features from raw log data,
enabling accurate anomaly detection and efficient root cause analysis.
Experiments were conducted on real-world log datasets, and the proposed method achieved high
accuracy and recall rates in detecting anomalies and identifying root causes. The results
demonstrate the effectiveness of the proposed method in optimizing the DevOps lifecycle through efficient log anomaly detection."
