车联网环境下基于Stacking集成学习的车辆异常行为检测方法

A Detection Method of Vehicular Abnormal Behaviors in V2X Environment Based on Stacking Ensemble Learning

针对车联网中的车辆异常行为的威胁,本文中融合了多种机器学习方法,提出了一种新型的适用于车联网的车辆异常行为检测方法。首先,基于Veins车联网仿真平台,模拟了DoS、Sybil等多种网络攻击,搭建了真实路况环境下遭受网络攻击的车联网场景,构建了车联网异常检测数据集;其次,采用Stacking集成学习思想,融合K近邻、决策树、多层感知机、AdaBoost、随机森林5种初级分类器建立集成检测模型;最后,利用交叉验证思想,使用5种初级分类器对训练集进行训练,并将初级分类器在验证集上的预测结果作为次级分类器的输入,将次级分类器的输出作为最终的预测结果。结果表明,本文提出的方法在不同攻击密度场景下对不同网络攻击都具有良好的检测效果,与其他单一分类器相比具有更好的检测结果,验证了本方法的有效性。

In view of the threat of abnormal vehicle behavior in V2X,a novel detection method of vehicle abnormal behavior suitable for V2X is proposed in this paper by fusing a variety of machine learning schemes.Firstly,based on Veins V2X simulation platform,various network attacks such as DoS,Sybil,etc.are simulated,the scenes of V2X subject to network attacks under real road conditions are constructed,and the detection da-ta set of abnormal vehicle behavior is built.Then by adopting the idea of stacking ensemble learning and fusing five primary classifiers of K-nearest neighbors,decision tree,multilayer perceptron,AdaBoost,and random forest,an ensemble detection model is set up.Finally,by utilizing the idea of cross-validation,the data set for training is trained by five primary classifiers,with the results of prediction on the data set for validation by primary classifiers as the input of secondary classifier,and the output of secondary classifier as the result of final prediction.The results show that the method proposed has a good detection effect on different network attacks in different scenes of attack density,and a better detection performance than other single classifiers,verifying the effectiveness of the method proposed.