Due to openness of wireless communication,Internet of vehicles(IoV)is vulnerable to many cyber-attacks such as denial of service,spoofing and fuzzy attacks.Therefore,random forest(RF)and gradient boosting decision tree-based stacking intrusion detection(RF-IDS)model was proposed.Firstly,the adaptive synthetic sampling(ADASYN)algorithm was adopted to generate more similar samples through the nearest neighbor sampling strategy in order to balance the training samples of different categories,and form a relatively symmetric dataset.Secondly,GBDT was used to evaluate the importance of features and select sample data with important features to build a light-weight classifier.Finally,the k-fold cross-validation stacking method was used to reduce the probability of overfitting.RF,GBDT and LightGBM classifiers serve were used as base-learner.The RG-IDS model was tested by CICIDS 2017 and NSL-KDD datasets.The experimental results demonstrate that RG-IDS model can achieve a higher F1-score.
Internet of vehiclesintrusion detectionADASYNGBDTstacking
NETL
NSTL
万方数据
