通信学报2026，Vol.47Issue(4)：40-53,14.DOI:10.11959/j.issn.1000-436x.2026078
基于指数平滑动态图的CAN总线入侵检测方法
CAN bus intrusion detection method based on exponentially smoothed dynamic graph
摘要
Abstract
The security of controller area network(CAN)bus is increasingly challenged by volatile and non-stationary communication patterns in modern vehicles,which traditional static detection methods have failed to capture.ES-DyGNN,an exponentially smoothed dynamic graph neural network,was proposed to capture the evolving relationships between electronic control unit(ECU).Unlike heuristic dynamic models,this method was underpinned by a rigorous ex-ponential smoothing graph operator that adaptively captured topological shifts.Closed form expansions for the dynamic adjacency sequences were derived and Frobenius norm convergence bounds that characterized the stability of the model were established.Furthermore,a theoretical lower bound on attack persistence was proven,ensuring subtle injections were detectable despite noise.Additionally,the model employed sinusoidal time embeddings and edge-conditional atten-tion to weigh both feature similarity and transition frequencies during message passing.Through extensive evaluations on benchmark datasets,it was demonstrated that an accuracy of over 99%was achieved by ES-DyGNN,while an infer-ence latency of less than 0.14 ms for each window was sustained.Through both rigorous theoretical analysis and exten-sive experimental validation,the proposed method demonstrates the feasibility of topology adaptation for automotive se-curity.关键词
CAN总线入侵检测/动态图神经网络/指数平滑/车载安全Key words
CAN bus intrusion detection/dynamic graph neural network/exponential smoothing/vehicular security分类
信息技术与安全科学引用本文复制引用
韦文杰,王建萍,陈彬,林福宏..基于指数平滑动态图的CAN总线入侵检测方法[J].通信学报,2026,47(4):40-53,14.基金项目
国家重点研发计划基金资助项目(No.2022YFB3104903) The National Key Research and Development Program of China(No.2022YFB3104903) （No.2022YFB3104903）
