Cybersecurity Metrics for AI-Based In-Vehicle Intrusion Detection Systems

Intrusion detection systems (IDSs) have become a crucial component in ensuring the security of in-vehicle networks. With the emergence of machine learning (ML), more flexible and efficient statistical methods have been introduced to the field of IDSs. However, the current metrics used to evaluate IDSs, including those based on the Common Vulnerability Scoring System (CVSS), need to adequately capture the unique characteristics of in-vehicle networks. To address this issue, we propose a new metric based on CVSS, designed explicitly for in-vehicle IDSs. Our metric considers cybersecurity information, including the severity of attacks encountered, to provide a more informed representation of performance. By prioritizing high performance on severe attacks, our metric provides a more accurate representation of an IDS's effectiveness in the context of in-vehicle networks. This new metric bridges the gap between ML and the unique cybersecurity challenges of in-vehicle networks, enhancing the overall security posture of the automotive industry.