Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

TL;DR

This comprehensive survey reviews machine learning techniques applied to enhance security in vehicular networks, covering attack taxonomy, challenges, and the effectiveness of various ML-based solutions.

Contribution

It provides a detailed taxonomy of security attacks, classifies ML techniques used in vehicular networks, and discusses their effectiveness and limitations.

Findings

ML techniques effectively detect security attacks in vehicular networks.

Modern vehicular architectures pose new security challenges.

ML-based methods face limitations like data scarcity and real-time constraints.

Abstract

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.