Source Linking Framework in Vehicular Networks for Security of Electric Vehicles using Machine Learning

Abstract

Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. The controller area network (CAN) protocol is an effective means of communication between in-vehicle control networks. However, the absence of basic security features of this protocol, like message authentication, makes it quite vulnerable to a wide range of attacks, including spoofing attacks. As traditional cybersecurity methods impose limitations in ensuring the confidentiality and integrity of transmitted messages via CAN bus, a new technique has emerged among others to approve its reliability in fully authenticating in-vehicle communication messages. At the physical layer of the communication system, the method of fingerprinting the messages is being implemented to connect the received signal to the transmitting Engine Control Unit (ECU). This paper introduces a new method to enhance the security of modern, fully autonomous electric vehicles. Errors due to digital to-analog converter (DAC) are used to estimate ECU-specific distortion distributions, which are utilized for transmitting node identification. A dataset collected from a CAN network with seven ECUs is used to evaluate the efficient performance of the suggested method. The experimental results indicate that kNNs achieved 99.2% accuracy in ECU detection and outperformed the rest of the classifiers.