Abstract:

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 data 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.

Key words: V2X, abnormal behavior detection, network attack, Stacking ensemble learning