Quantum-based Automotive Threat Intelligence and Countermeasures

Due to the increasing amount of software and hardware in connected and autonomous cars, the attack surface is growing, which increases the risk of security attacks. Researchers proposed machine learning or deep learning techniques to identify threats in in-vehicle networks. However, using these techniques is not enough to support the automotive industry since new processes or techniques must be conceptualized to make automotive systems more secure. Therefore, this research work presents a methodology, Quantum-based Automotive Threat Intelligence and Countermeasures (QUANTICAR), that integrates quantum optimization for CAN bus Intrusion Detection and the National Vulnerability Database (NVD) to understand the automotive attacks. In the first phase, QUANTICAR identifies the different types of attacks and then, based on the specific attack class, extracts new knowledge using the NVD. This contributes not only to improving attack detection but also to developing an Automotive Knowledge Base that can support developers and security experts in the secure development of automotive components in compliance with ISO/SAE 21434.