信息安全研究2025，Vol.11Issue(10)：907-916,10.DOI:10.12379/j.issn.2096-1057.2025.10.05
基于TCN-GAN的时序流量异常检测
TCN-GAN-based Temporal Traffic Anomaly Detection
摘要
Abstract
In recent years,generative adversarial networks have been widely used in the field of temporal anomaly detection.However,temporal data often has complex time-dependence,and problems such as gradient vanishing and training instability are common in existing anomaly detection models.To this end,this paper proposes an unsupervised temporal traffic anomaly detection model based on the combination of temporal convolutional network(TCN)and GAN.The model uses TCN as the infrastructure of generator and discriminator,which can effectively capture the temporal features of the temporal traffic data.During the anomaly detection process,the model constructs an anomaly scoring function based on the reconstruction loss and discriminator loss,and performs anomaly judgment by setting a threshold,thus improving the accuracy of anomaly detection.To verify the performance of the proposed model,experiments are conducted on five different types of datasets.The results show that the average F1 score of the proposed model is 11.02％higher than that of the TAnoGAN model.关键词
时序卷积网络/生成对抗网络/无监督/异常检测/时序流量Key words
TCN/GAN/unsupervised/anomaly detection/temporal traffic分类
信息技术与安全科学引用本文复制引用
李琛,林维,许力..基于TCN-GAN的时序流量异常检测[J].信息安全研究,2025,11(10):907-916,10.基金项目
国家自然科学基金项目(62471139) （62471139）
中央引导地方科技发展专项(2023L3007) （2023L3007）
福建省科技项目(2022G02003) （2022G02003）
