DeepVCM: A Deep Learning Based Intrusion Detection Method in VANET

With the rapid development in smart vehicles, the security and privacy issues of the Vehicular Ad-hoc Network (VANET) have drawn significant attention. Devices in an On-Board Unit (OBU) access to the internet through the Vehicular Communication Module (VCM), hence a real-time and accurate intrusion detection method is favored to be applied in VCM. In this paper, we present a Deep Learning (DL) based end-to-end intrusion detection method to automatically detect malware traffic for OBUs. Different from previous intrusion detection methods, our proposed method only requires raw traffic instead of private information features extracted by the human. The performance is compared with previous methods on a public dataset and a simulated real-life VANET dataset. Experimental results show that our method can attain a higher performance with a lower resources requirement.