计算机工程2025，Vol.51Issue(4)：47-56,10.DOI:10.19678/j.issn.1000-3428.0070231
融合动态图嵌入和Transformer自编码器的网络异常检测
Network Anomaly Detection Integrating Dynamic Graph Embedding and Transformer Autoencoder
摘要
Abstract
Network anomaly detection aims to promptly identify and respond to malicious activities and potential threats within networks.Most existing graph-embedding-based methods are designed for static graphs and neglect fine-grained temporal information,thus failing to capture the continuity of dynamic network behaviors and diminishing the effectiveness of network anomaly detection.To enhance the efficiency and accuracy of dynamic network anomaly detection,this study proposes a novel method integrating dynamic graph embedding and Transformer autoencoders.This method leverages temporal-walk-based graph embedding to capture the topological structure and detailed temporal information of the network.It incorporates a Transformer autoencoder with contrastive loss to optimize node embeddings and effectively capture long-term dependencies and global information.This integration enhances the model's ability to perceive dynamic networks,facilitating better detection of time-evolving events and the identification of malicious behaviors.The effectiveness of this method is validated through extensive experiments conducted on two publicly available datasets in network security.Its superior performance on the LANL-2015 dataset is indicated with a True Positive Rate(TPR)of 94.3％,False Positive Rate(FPR)of 5.7％,and an Area Under the Curve(AUC)of 98.3％.Further,on the OpTC dataset,the method achieves a TPR of 99.9％,a FPR of 0.01％,and an AUC of 99.9％.These results demonstrate that the proposed method effectively learns the topology and temporal dependencies of dynamic networks,thereby accurately identifying network anomalies.关键词
动态图嵌入/Transformer自编码器/网络异常检测/恶意行为/长短期时间依赖Key words
dynamic graph embedding/Transformer autoencoder/network anomaly detection/malicious behavior/long and short-term time-dependence分类
计算机与自动化引用本文复制引用
张安勤,丁志锋..融合动态图嵌入和Transformer自编码器的网络异常检测[J].计算机工程,2025,51(4):47-56,10.基金项目
广东省人文社会科学重点研究基地——汕头大学地方政府发展研究所开放基金(07422002). （07422002）
