  Significant progress has been made towards deploying Vehicle-to-Everything
(V2X) technology. Integrating V2X with 5G has enabled ultra-low latency and
high-reliability V2X communications. However, while communication performance
has enhanced, security and privacy issues have increased. Attacks have become
more aggressive, and attackers have become more strategic. Public Key
Infrastructure proposed by standardization bodies cannot solely defend against
these attacks. Thus, in complementary of that, sophisticated systems should be
designed to detect such attacks and attackers. Machine Learning (ML) has
recently emerged as a key enabler to secure our future roads. Many V2X
Misbehavior Detection Systems (MDSs) have adopted this paradigm. Yet, analyzing
these systems is a research gap, and developing effective ML-based MDSs is
still an open issue. To this end, this paper present a comprehensive survey and
classification of ML-based MDSs. We analyze and discuss them from both security
and ML perspectives. Then, we give some learned lessons and recommendations
helping in developing, validating, and deploying ML-based MDSs. Finally, we
highlight open research and standardization issues with some future directions.
