  Machine Learning (ML) has emerged as an attractive and viable technique to
provide effective solutions for a wide range of application domains. An
important application domain is vehicular networks wherein ML-based approaches
are found to be very useful to address various problems. The use of wireless
communication between vehicular nodes and/or infrastructure makes it vulnerable
to different types of attacks. In this regard, ML and its variants are gaining
popularity to detect attacks and deal with different kinds of security issues
in vehicular communication. In this paper, we present a comprehensive survey of
ML-based techniques for different security issues in vehicular networks. We
first briefly introduce the basics of vehicular networks and different types of
communications. Apart from the traditional vehicular networks, we also consider
modern vehicular network architectures. We propose a taxonomy of security
attacks in vehicular networks and discuss various security challenges and
requirements. We classify the ML techniques developed in the literature
according to their use in vehicular network applications. We explain the
solution approaches and working principles of these ML techniques in addressing
various security challenges and provide insightful discussion. The limitations
and challenges in using ML-based methods in vehicular networks are discussed.
Finally, we present observations and lessons learned before we conclude our
work.
