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  • Open Access

    ARTICLE

    Log-Based Anomaly Detection of System Logs Using Graph Neural Network

    Eman Alsalmi, Abeer Alhuzali*, Areej Alhothali

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071012 - 09 December 2025

    Abstract Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems. Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems. In this study, we propose a hybrid model, BertGCN, that integrates BERT-based contextual embedding with Graph Convolutional Networks (GCNs) to identify anomalies in raw system logs, thereby eliminating the need for log parsing. The BERT module captures semantic representations of log messages, while the GCN models the structural relationships among log entries through a text-based graph. This combination More >

  • Open Access

    ARTICLE

    Logformer: Cascaded Transformer for System Log Anomaly Detection

    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 - 05 January 2023

    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… More >

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