摘要
近年来,车辆恶意位置攻击检测中主要使用深度学习技术.然而,深度学习模型训练耗时巨大、参数众多,基于深度学习的检测方法缺乏可扩展性,无法适应车联网不断产生新数据的需求.为了解决以上问题,创新地将增量学习算法引入车辆恶意位置攻击检测中,解决了上述问题.首先从采集到的车辆信息数据中提取关键特征;然后,构建恶意位置攻击检测系统,利用岭回归近似快速地计算出车联网恶意位置攻击检测模型;最后,通过增量学习算法对恶意位置攻击检测模型进行更新和优化,以适应车联网中新生成的数据.实验结果表明,相比SVM,KNN,ANN等方法具有更优秀的性能,能够快速且渐进地更新和优化旧模型,提高系统对恶意位置攻击行为的检测精度.
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.
作者
江荣旺
魏爽
龙草芳
杨明
Jiang Rongwang;Wei Shuang;Long Caofang;Yang Ming(Sanya College,Sanya,Hainan 572022;Academician Rong Chunming Workstation,Sanya,Hainan 572022)
出处
《信息安全研究》
CSCD
北大核心
2024年第3期268-276,共9页
Journal of Information Security Research
基金
海南省自然科学基金青年项目(620QN287,621QN0901)
海南省自然科学基金高层次人才项目(621RC602)
三亚学院重大专项课题(USY22XK-04)
三亚学院校级项目(USYYB22-07)
海南省重点研发项目(ZDYF2023GXJS007)
海南省教育厅重点科研项目(Hnky2023ZD-14)。
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
VANET
location attack detection
incremental learning
deep learning
machine learning
