Research on System Log Anomaly Detection Method Based on Graph Neural Network

System log anomaly detection is crucial to ensure the safety of system operation. The anomaly detection method based on graph neural network constructs a dynamically evolving graph structure through <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\log$</tex> sequences, extracts node features by using multi-attention mechanism, and calculates anomaly scores by combining with Mahalanobis distance. It achieves 95.8% detection accuracy on HDFS dataset, which is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5. 7 \% - 1 2. 3 \%}$</tex> improvement over existing methods. Experiments show that the dynamic graph updating mechanism and the multi-head attention structure significantly improve the model performance, with a 43.2 % improvement in computational efficiency. The method provides a new solution for large-scale syslog anomaly detection.