Adanomaly: Adaptive Anomaly Detection for System Logs with Adversarial Learning
Jiaxing Qi, Zhongzhi Luan, Shaohan Huang, Yukun Wang, Carol Fung, Hailong Yang, Depei Qian · NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium · 2022
Logs are commonly used to record the running status of application service systems. Log-based anomaly detection in the system can significantly improve the quality of system services by avoiding catastrophic failures. However, existing log-based anomaly detection methods do not consider class imbalance, which is a common challenge in anomaly detection. In addition, existing methods require hyperparameters in the detection stage, which negatively impacts the accuracy of detection. In this paper, we propose a novel log-based anomaly detection method named Adanomaly, which uses the BiGAN model to extract features and use the ensemble method to detect anomalies. Experimental demonstrate that Adanomaly can detect system abnormalities efficiently, and outperform recall and accuracy compared to other methods.