OMLog: Online Log Anomaly Detection for Evolving System with Meta-learning
Jiyu Tian, Mingchu Li, Zumin Wang, Liming Chen, Jing Qin, Runfa Zhang

TL;DR
OMLog introduces a semi-supervised online meta-learning approach for real-time log anomaly detection, effectively handling distribution shifts in evolving systems and outperforming state-of-the-art methods on public datasets.
Contribution
The paper proposes OMLog, a novel online meta-learning method with distribution shift detection for improved real-time log anomaly detection in dynamic systems.
Findings
Achieves F1-Score of 93.7% on HDFS dataset
Surpasses state-of-the-art LAD methods in detection efficiency
Effectively detects distribution changes in log sequences
Abstract
Log anomaly detection (LAD) is essential to ensure safe and stable operation of software systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with evolving systems. To construct a real-time and reliable online log anomaly detection model, we propose OMLog, a semi-supervised online meta-learning method, to effectively tackle the distribution shift issue caused by changes in log event types and frequencies. Specifically, we introduce a maximum mean discrepancy-based distribution shift detection method to identify distribution changes in unseen log sequences. Depending on the identified distribution gap, the method can automatically trigger online fine-grained detection or…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Time Series Analysis and Forecasting
