Enhancing Data Security in Autonomous Vehicle Communication Networks
DOI:
https://doi.org/10.60087/jklst.vol2.n3.p521Keywords:
machine learning, cryptography, cyber-attacks, cybersecurity, intrusion detection systemAbstract
In today's driving landscape, in-vehicle communication has become a cornerstone, facilitated by the proliferation of sensor-centric communication and computing devices within vehicles. These systems serve various functions such as vehicle monitoring, reducing physical wiring, and enhancing driving efficiency. However, the existing literature on cybersecurity for in-vehicle communication systems lacks dedicated solutions to mitigate in-vehicle cyber risks effectively. Current approaches primarily rely on protocol-specific security techniques, lacking a comprehensive security framework for in-vehicle communication. This paper critically examines the literature on cybersecurity for in-vehicle communication, focusing on technical architecture, methodologies, challenges, and potential solutions.
The paper presents an in-depth analysis of in-vehicle communication network architecture, outlining key components, interfaces, and associated technologies. Protocols utilized in in-vehicle communication are classified based on their characteristics and usage types. Furthermore, security solutions for in-vehicle communication are critically reviewed, encompassing machine learning, cryptography, and port-centric techniques. A multi-layer secure framework is proposed as a protocol and use case-independent solution for in-vehicle communication security.Lastly, the paper identifies open challenges and outlines future research directions for enhancing cybersecurity in in-vehicle communication.
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Copyright (c) 2023 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)
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