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Open Access

ARTICLE

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

Eman Alsalmi, Abeer Alhuzali*, Areej Alhothali
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
* Corresponding Author: Abeer Alhuzali. Email: email
(This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071012

Received 29 July 2025; Accepted 29 September 2025; Published online 30 October 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 enables BertGCN to capture both the contextual and semantic characteristics of log data. BertGCN showed excellent performance on the HDFS and BGL datasets, demonstrating its effectiveness and resilience in detecting anomalies. Compared to multiple baselines, our proposed BertGCN showed improved precision, recall, and F1 scores.

Keywords

Log anomaly detection; BERT; graph convolutional network; system logs; explainable anomaly detection

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