Poster: Cyber Resiliency Metrics for AI-Based In-Vehicle Intrusion Detection Systems

With the continuous development of Autonomous Vehicles (AVs), Intrusion Detection Systems (IDSs) became essential to ensure the security of in-vehicle (IV) networks. In the literature, classic machine learning (ML) metrics used to evaluate AI-based IV-IDSs present significant limitations and fail to assess their robustness fully. To address this, our study proposes a set of cyber resiliency metrics adapted from MITRE's Cyber Resiliency Metrics Catalog, tailored for AI-based IV-IDSs. We introduce specific calculation methods for each metric and validate their effectiveness through a simulated intrusion detection scenario. This approach aims to enhance the evaluation and resilience of IV-IDSs against advanced cyber threats and contribute to safer autonomous transportation.