Shiming He¹, Tuo Deng¹, Bowen Chen¹, R. Simon Sherratt², Jin Wang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 517-541, 2023, DOI:10.32604/cmc.2023.037392

Abstract Log anomaly detection is an important paradigm for system troubleshooting. Existing log anomaly detection based on Long Short-Term Memory (LSTM) networks is time-consuming to handle long sequences. Transformer model is introduced to promote efficiency. However, most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing, which introduces parsing errors. They only extract simple semantic feature, which ignores other features, and are generally supervised, relying on the amount of labeled data. To overcome the limitations of existing methods, this paper proposes a novel unsupervised log anomaly detection method based on multi-feature (UMFLog). UMFLog includes… More >

Feilu Hang¹, Wei Guo¹, Hexiong Chen¹, Linjiang Xie¹, Chenghao Zhou^2,*, 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 >