融合ResNet和SRU的网络数据流实时异常流量检测技术OA

[Objective]With the rapid development of the industrial internet,detecting abnormal traffic in network data streams has become a critical task for ensuring network security.Traditional machine learning models struggle with feature extraction and generalization,making it difficult to handle high-dimensional,diverse,and massive network traffic data.To address these challenges,this study proposed a novel abnormal traffic detection method combining a simple recurrent unit(SRU)with an improved residual network(ResNet).This method aims to enhance detection accuracy and efficiency through spatiotemporal feature extraction while mitigating issues such as overfitting and the gradient vanishing problem,thus offering a more efficient and reliable solution for network abnormal traffic detection.[Methods]A deep learning model integrating SRU and the improved ResNet was constructed.The SRU network handled data screening and temporal feature extraction,enabling efficient parallel computation via reset and forget gates,which significantly boosted training speed.The improved ResNet adopted an atrous residual structure,expanding the receptive field with atrous convolution to enhance feature extraction and alleviate gradient vanishing.By combining these networks,both spatial and temporal features of network traffic data were captured comprehensively.Experiments were conducted on the KDD Cup 99 dataset for binary classification to evaluate the model's performance.[Results]The experimental results show that the ResNet-SRU model achieves a classification accuracy of 98.89％and a precision of 98.66％on the KDD Cup 99 dataset.Compared to methods such as CNN-LSTM,ResNet-GRU,and CNN-GRU,it achieves approximately a 1％improvement.During training,the model demonstrates faster convergence and superior stability.It outperforms comparative models in accuracy,precision,recall,and AUC,highlighting its effectiveness and robustness in abnormal traffic detection.Although training and testing times are slightly longer,the significant improvement in detection performance justifies this trade-off.[Conclusions]The abnormal traffic detection method based on ResNet and SRU shows remarkable advantages in processing high-dimensional network traffic data.By integrating the advantages of atrous residual structures for spatial modeling with SRU's temporal feature extraction,it effectively overcomes the limitations of traditional models in feature extraction and generalization,enhancing detection accuracy and efficiency.However,the model's parameter scale and computational cost still require optimization.Future research will focus on lightweight model design,improving detection performance for imbalanced samples,and further reducing computational overhead to enhance its practical application value.