计算机应用与软件2026，Vol.43Issue(4)：352-359,8.DOI:10.3969/j.issn.1000-386x.2026.04.049
结合对抗自编码器与聚类方法的网络异常检测
COMBINING ADVERSARIAL AUTO-ENCODER AND CLUSTERING FOR NETWORK TRAFFIC ANOMALY DETECTION
摘要
Abstract
To address the problems of inadequate feature extraction,poor generalization ability and neglect of correlation between non-adjacent features in existing IoT network device anomaly detection,we propose an unsupervised network traffic anomaly detection method combining adversarial self-encoder and feature clustering.We used graph attention network and gated temporal convolutional network for spatiotemporal feature extraction,and proposed a multi-stage layer-by-layer propagation mechanism to enhance the feature extraction of the original data by the model.The model's false alarm rate was effectively reduced by using adversarial exercises on the self-encoders to amplify the anomaly scores,and the K-means algorithm was used for feature clustering.Extensive experiments were conducted on four datasets in this paper to verify the effectiveness of the proposed method.关键词
网络流量/异常检测/无监督/图神经网络/对抗训练Key words
Network traffic/Anomaly detection/Unsupervised/Graph neural network/Adversarial training分类
信息技术与安全科学引用本文复制引用
胡游君,李马峰,邱文元,富思,王虹岚,时宽治,李静..结合对抗自编码器与聚类方法的网络异常检测[J].计算机应用与软件,2026,43(4):352-359,8.基金项目
国家电网有限公司总部科技项目(5108-202218280A-2-152-XG). （5108-202218280A-2-152-XG）
