SA-ResNet:An Intrusion Detection Method Based on Spatial Attention Mechanism and Residual Neural Network Fusion  

作　　者：Zengyu Cai Yuming Dai Jianwei Zhang Yuan Feng 

机构地区：[1]School of Computer Science and Technology,Zhengzhou University of Light Industry,Zhengzhou,450066,China [2]College of Software Engineering,Zhengzhou University of Light Industry,Zhengzhou,450066,China [3]Faculty of Information Engineering,Xuchang Vocational Technical College,Xuchang,461000,China [4]School of Electronic Information,Zhengzhou University of Light Industry,Zhengzhou,450066,China

出　　处：《Computers, Materials & Continua》2025年第5期3335-3350,共16页计算机、材料和连续体(英文)

基　　金：supported by National Natural Science Foundation of China(62473341);Key Research and Development Special Project of Henan Province(221111210500);Key Research and Development Special Project of Henan Province(242102211071,242102210142,232102211053).

摘　　要：The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network security.Intrusion Detection Systems(IDS)are essential for safeguarding network integrity.To address the low accuracy of existing intrusion detection models in identifying network attacks,this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network(SA-ResNet).Utilizing residual connections can effectively capture local features in the data;by introducing a spatial attention mechanism,the global dependency relationships of intrusion features can be extracted,enhancing the intrusion recognition model’s focus on the global features of intrusions,and effectively improving the accuracy of intrusion recognition.The proposed model in this paper was experimentally verified on theNSL-KDD dataset.The experimental results showthat the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%,and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network(CNN)models.

关 键 词：Intrusion detection deep learning residual neural network spatial attention mechanism 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]