Log Anomaly Detection by Adversarial Autoencoders With Graph Feature Fusion
Yuxia Xie, Kai Yang · IEEE Transactions on Reliability · 2023
The exponential growth of scale and complexity in distributed systems necessitates significant maintenance efforts. Logs play an indispensable role in system operation and maintenance since they record crucial runtime information. However, recent studies on log anomaly detection have primarily focused on deep learning methods, which entail high computational complexity for learning temporal and semantic features from logs. Moreover, most deep learning-based approaches for log anomaly detection require supervised training, which is labor intensive. To address these challenges, this article proposes a framework calledGAE-Log.GAE-Logleverages event graphs and knowledge graphs to model logs comprehensively. By integrating temporal dynamics through event graphs and incorporating contextual information from knowledge graphs,GAE-Logenhances the understanding of the system's status. Moreover,GAE-Logemploys adversarial training of autoencoders for anomaly detection on logs. The effectiveness ofGAE-Logis evaluated through an ablation study and comprehensive comparisons using both public and synthetic log datasets. The results demonstrate thatGAE-Logoutperforms state-of-the-art methods in log anomaly detection, achieving significant performance improvements.