Anomaly detection in virtual machine logs against irrelevant attribute interference

• Published in: PloS one Vol. 20; no. 1; p. e0315897
• Main Authors: Zhang, Hao, Zhou, Yun, Xu, Huahu, Shi, Jiangang, Lin, Xinhua, Gao, Yiqin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.01.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Anomalies; Automation; Biology and Life Sciences; Clustering; Computer and Information Sciences; Debugging; Engineering and Technology; Error messages; Feature extraction; Humans; Identification methods; Information processing; Long short-term memory; Machine learning; Methods; Operations management; Pareto optimum; Parsing algorithms; Physical Sciences; Research and Analysis Methods; Robustness; Social Sciences; Statistical methods; Support Vector Machine; Support vector machines; System failures; Virtual computer systems; Virtual environments; China

Virtual machine logs are generated in large quantities. Virtual machine logs may contain some abnormal logs that indicate security risks or system failures of the virtual machine platform. Therefore, using unsupervised anomaly detection methods to identify abnormal logs is a meaningful task. However...