聊城大学学报（自然科学版）2025，Vol.38Issue(4)：507-515,526,10.DOI:10.19728/j.issn1672-6634.2024090010
基于GConvGRU-GAT的车联网安全性能智能预测方法研究
Research on intelligent prediction method of internet of vehicles security performance based on GConvGRU-GAT
摘要
Abstract
With the continuous development of 5G technology and the Internet of Things,the Internet of Vehicles business is emerging in an endless stream,and its application scenarios are becoming more and more complex and changeable.In the communication process of Internet of Vehicles,the security perform-ance is affected by the complex and ever-changing environment of the vehicle networking,making it diffi-cult to make real-time and accurate predictions.Therefore,this paper proposes an intelligent prediction method for the security performance of the Internet of Vehicles based on GConvGRU-GAT in this paper.Firstly,under the N-Nakagami channel,a model of the secure communication system of the Internet of Vehicles was established,and the security performance was analyzed by detecting the signal-to-noise ratio of the communication link,and the influence of different influencing factors on the security performance was effectively analyzed,and the communication dataset was constructed.In order to better predict the communication system in real time,this paper integrates Graph Attention Network(GAT),Graph Conv-olutional Network(GCN)and Gated Recurrent Unit(GRU)to design a mobile security performance pre-diction network model based on GConvGRU-GAT by integrating Graph Attention Network(GAT),Graph Convolutional Network(GCN)and Gated Recurrent Unit(GRU).Experimental results show that compared with other algorithms,the GConvGRU-GAT algorithm has better prediction effect,and the per-formance is improved by 84.6%compared with the GCN model.关键词
车联网/安全通信/安全性能预测/图注意力神经网络Key words
internet of vehicles/secure communications/security performance prediction/graph attention neural network分类
信息技术与安全科学引用本文复制引用
李天成,薛培友,徐凌伟..基于GConvGRU-GAT的车联网安全性能智能预测方法研究[J].聊城大学学报（自然科学版）,2025,38(4):507-515,526,10.基金项目
国家自然科学基金项目(62201313) （62201313）
安徽省普通高校交通信息与安全重点实验室开放课题(JTX202404)资助 （JTX202404）
