信息安全研究2024，Vol.10Issue(3)：268-276,9.DOI:10.12379/j.issn.2096-1057.2024.03.11
基于增量学习的车联网恶意位置攻击检测研究
Research on Location Attack Detection of VANET Based on Incremental Learning
摘要
Abstract
In recent years,deep learning has been widely employed in the detection of malicious position attacks on vehicles.However,deep learning models necessitate extensive training time and possess a large number of parameters.Detection methods based on deep learning lack scalability and cannot accommodate the needs of continuously generated new data in vehicular networks.To address these issues,this paper innovatively introduces incremental learning algorithms into the detection of malicious position attacks on vehicles to solve the above problems.This approach first extracts key features from the collected vehicle information data.Subsequently,a malicious position attack detection system is constructed,utilizing ridge regression to quickly approximate the vehicular network's malicious position attack detection model.Finally,the incremental learning algorithm is applied to update and optimize the malicious position attack detection model to adapt to newly generated data in the vehicular network.Experimental results demonstrate that this method surpasses other methods such as SVM,KNN,and ANN in terms of performance.It can swiftly and progressively update and optimize the old model,thereby enhancing the system's detection accuracy for malicious position attack behaviors.关键词
车联网/恶意位置攻击检测/增量学习/深度学习/机器学习Key words
VANET/location attack detection/incremental learning/deep learning/machine learning分类
信息技术与安全科学引用本文复制引用
江荣旺,魏爽,龙草芳,杨明..基于增量学习的车联网恶意位置攻击检测研究[J].信息安全研究,2024,10(3):268-276,9.基金项目
海南省自然科学基金青年项目(620QN287,621QN0901) （620QN287,621QN0901）
海南省自然科学基金高层次人才项目(621RC602) （621RC602）
三亚学院重大专项课题(USY22XK-04) （USY22XK-04）
海南省重点研发项目(ZDYF2023GXJS007) （ZDYF2023GXJS007）
三亚学院校级项目(USYYB22-07) （USYYB22-07）
海南省教育厅重点科研项目(Hnky2023ZD-14) （Hnky2023ZD-14）
