AutoML based Stacking Integration for Vehicle Network Anomaly Detection
Yibo Du, Peng Wang, Yu-Feng Li · 2023
Intelligent Connected Vehicle (ICV) is a new industrial form that deeply integrates industries such as automobiles, electronics, and information communication. However, abnormal in vehicle networks may lead to serious safety and performance issues. To improve the accuracy and efficiency of anomaly detection in vehicle networks, this paper proposes an Automated Machine Learning (AutoML) based on Stacking integration method. The proposed anomaly detection is implemented on the standard dataset, and the results show that our method has higher detection accuracy and robustness than the traditional single model. After automatic feature engineering, further select the most relevant features, reduce the calculation time, and significantly improve the model efficiency.