  AI/ML-based intrusion detection systems (IDSs) and misbehavior detection
systems (MDSs) have shown great potential in identifying anomalies in the
network traffic of networked autonomous systems. Despite the vast research
efforts, practical deployments of such systems in the real world have been
limited. Although the safety-critical nature of autonomous systems and the
vulnerability of learning-based techniques to adversarial attacks are among the
potential reasons, the lack of objective evaluation and feasibility assessment
metrics is one key reason behind the limited adoption of these systems in
practical settings. This survey aims to address the aforementioned limitation
by presenting an in-depth analysis of AI/ML-based IDSs/MDSs and establishing
baseline metrics relevant to networked autonomous systems. Furthermore, this
work thoroughly surveys recent studies in this domain, highlighting the
evaluation metrics and gaps in the current literature. It also presents key
findings derived from our analysis of the surveyed papers and proposes
guidelines for providing AI/ML-based IDS/MDS solution approaches suitable for
vehicular network applications. Our work provides researchers and practitioners
with the needed tools to evaluate the feasibility of AI/ML-based IDS/MDS
techniques in real-world settings, with the aim of facilitating the practical
adoption of such techniques in emerging autonomous vehicular systems.
