Attack detection model based on stacking ensemble learning for Internet of vehicles

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 lightweight classifier. Finally, the <italic>k</italic>-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.