Feilu Hang1, Wei Guo1, Hexiong Chen1, Linjiang Xie1, Chenghao Zhou2,*, Yao Liu2
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 517-529, 2023, DOI:10.32604/cmes.2023.025774
Abstract Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points. These logs are valuable for analyzing performance issues and understanding the status of the system. Anomaly detection plays an important role in service management and system maintenance, and guarantees the reliability and security of online systems. Logs are universal semi-structured data, which causes difficulties for traditional manual detection and pattern-matching algorithms. While some deep learning algorithms utilize neural networks to detect anomalies, these approaches have an over-reliance on manually designed features, resulting in the effectiveness of anomaly detection depending… More >
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