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

• URL: http://arxiv.org/abs/2201.10500v1
• Date: Tue, 25 Jan 2022 17:48:57 GMT
• Title: A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks
• Authors: Abdelwahab Boualouache and Thomas Engel
• Abstract summary: Integrating V2X with 5G has enabled ultra-low latency and high-reliability V2X communications. Attacks have become more aggressive, and attackers have become more strategic. 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.
• Score: 4.410803831098062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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