Intrusion Detection System for Internet of Vehicles Based on Ensemble Learning and CNN

With the combination of network and automotive technology, more and more modern cars are controlled based on electronic control units (ECUs). And multiple ECUs in modern cars are interconnected and cooperate through the in-vehicle network. But such in-vehicle networks do not consider potential security issues, which might lead to property damage and life threat. An intrusion Detection System (IDS) is a method to detect anomalies and warn about network intrusions. In this study, an IDS model based on Convolutional Neural Networks (CNNs) and ensemble learning is proposed. Meanwhile, to further improve the capability of the proposed IDS model, the structure of some CNN basic models is modified. Finally, the proposed model is validated on a representative standard Internet of Vehicles (IoV) dataset Car-Hacking Dataset. In the experiment, the proposed model achieves 100% accuracy and fl-score, and the detection time is from 1.0ms to 2.8ms, which demonstrates that the proposed IDS model detects the effectiveness of network intrusions.