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Significant progress has been made towards deploying Vehicle-to-Everything(V2X) technology. Integrating V2X with 5G has enabled ultra-low latency andhigh-reliability V2X communications. However, while communication performancehas enhanced, security and privacy issues have increased. Attacks have becomemore aggressive, and attackers have become more strategic. Public KeyInfrastructure proposed by standardization bodies cannot solely defend againstthese attacks. Thus, in complementary of that, sophisticated systems should bedesigned to detect such attacks and attackers. Machine Learning (ML) hasrecently emerged as a key enabler to secure our future roads. Many V2XMisbehavior Detection Systems (MDSs) have adopted this paradigm. Yet, analyzingthese systems is a research gap, and developing effective ML-based MDSs isstill an open issue. To this end, this paper present a comprehensive survey andclassification of ML-based MDSs. We analyze and discuss them from both securityand ML perspectives. Then, we give some learned lessons and recommendationshelping in developing, validating, and deploying ML-based MDSs. Finally, wehighlight open research and standardization issues with some future directions.

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks