计算机与现代化Issue(8)：39-47,9.DOI:10.3969/j.issn.1006-2475.2025.08.006
基于图神经网络的异常事件预测方法
Anomalous Event Prediction Approach Using Graph Neural Network
摘要
Abstract
Log-oriented anomalous event prediction provides important support for security diagnosis,intelligent operation and maintenance of complex systems.Most of the existing mainstream anomalous event prediction methods based on deep learning technology capture sequence features from the local perspective of event sequence segments and the feature types are relatively single,resulting in low prediction accuracy.To solve this problem,an anomalous event prediction method based on graph neural network is proposed.The log sequence is represented as a graph structure with log events as nodes and the relationship between events as edges,so that it can simultaneously depict the log sequence from the perspective of semantics,statistics and event rela-tionship to capture its spatio-temporal dynamic characteristics to improve the prediction performance.On this basis,the anomaly prediction task is transformed into a graph classification problem,and an anomaly prediction model based on graph isomorphic network is established by training graph neural network,which can more accurately capture the difference between the log se-quence before the failure and the log sequence under normal conditions,and further improve the performance of anomaly predic-tion.The experimental results on three benchmark datasets show that the average F1 of the proposed method is 0.958,which is better than the comparison methods,and it can accurately predict anomalous events for early warning.关键词
日志解析/异常预测/图神经网络/事件挖掘Key words
log parsing/anomaly prediction/graph neural network/event mining分类
信息技术与安全科学引用本文复制引用
李岩,冒佳明,王梓莹,顾智敏,姜海涛..基于图神经网络的异常事件预测方法[J].计算机与现代化,2025,(8):39-47,9.基金项目
国网江苏省电力有限公司科技项目(J2023180) （J2023180）
