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.

Proactive cyber-risk assessment is gaining momentum due to the wide range of sectors that can benefit from the prevention of cyber-incidents by preserving integrity, confidentiality, and the availability of data. The rising attention to cybersecurity also results from the increasing connectivity of cyber–physical systems, which generates multiple sources of uncertainty about emerging cyber-vulnerabilities. This work introduces a robust statistical framework for quantitative and qualitative reasoning under uncertainty about cyber-vulnerabilities and their prioritisation. Specifically, we take advantage of mid-quantile regression to deal with ordinal risk assessments, and we compare it to current alternatives for cyber-risk ranking and graded responses. For this purpose, we identify a novel accuracy measure suited for rank invariance under partial knowledge of the whole set of existing vulnerabilities. The model is tested on both simulated and real data from selected databases that support the evaluation, exploitation, or response to cyber-vulnerabilities in realistic contexts. Such datasets allow us to compare multiple models and accuracy measures, discussing the implications of partial knowledge about cyber-vulnerabilities on threat intelligence and decision-making in operational scenarios.