Deep Learning-Based Intrusion Detection System for Internet of Vehicles

The growth of the Internet of Things (IoT) has resulted in several revolutionary applications, such as smart cities, cyber-physical systems, and the Internet of vehicles (IoV). Within the IoV infrastructure, vehicles are comprised of various electronic intelligent sensors or devices used to obtain data and communicate the necessary information with their surroundings. One of the major concerns about the implementation of these sensors or devices is data vulnerability; thus, it is necessary to present a solution that provides security, trust, and privacy to communicating entities and to secure vehicle data from malicious entities. In modern vehicles, the controller area network (CAN) is a fundamental scheme for controlling the interaction among different in-vehicle network sensors. However, not enough security features are present that support data encryption, authorization, and authentication mechanisms to secure the network from cyber or malicious intrusions such as denial of service and fuzzy attacks. An intrusion detection system is presented in this work based on the deep learning architecture to protect the CAN bus in vehicles. The VGG architecture is used and trained for different network intrusion patterns in order to detect malicious attacks. The experiments are performed using the CAN-intrusion-dataset. The experimental findings demonstrate that the presented deep learning system significantly reduces the false positive rate (FPR) compared to the conventional machine learning techniques. The overall accuracy of the system is 96% with FPR of 0.6%.