Time-Aware Log Anomaly Detection Based on Growing Self-organizing Map
Authors
Fedotov, D.; Kuchař, J.; Vitvar, T.
Year
2023
Published
Service-Oriented Computing. Springer, Cham, 2023. p. 169-177. ISSN 0302-9743. ISBN 978-3-031-48420-9.
Type
Proceedings paper
Departments
Annotation
A software system generates extensive log data, reflecting its workload and potential failures during operation. Log anomaly detection algorithms use this data to identify deviations in system behavior, especially when errors occur. Workload patterns can vary with time, depending on factors like the time of day or day of the week, affecting log entry volumes. Thus, it’s essential for log anomaly detection to consider temporal information that captures workload variations. This paper introduces a novel log anomaly detection method that incorporates such time information and demonstrates how smaller models enhance anomaly detection precision. We evaluate this method on a high-throughput production workload of a software system, showcasing its superior performance over conventional log anomaly detection methods.