Cybersecurity within the Internet of Vehicles (IoV): Using Machine Learning to Protect Against External Attacks
Open Access DepositedThe Internet of Vehicles (IoV), a cloud-connected network comprising vehicles, smartphones, people, and roadside infrastructure, serves as the catalyst for Intelligent Transportation Systems (ITS). Advancements in automotive architecture, increased integration of connected vehicle (CV) technology, and the adoption of wireless communication protocols are the primary drivers of the progression in external vehicle communication.Efficient improvements in growing transportation services, including enhanced traffic predictions, road safety measures, and driver convenience, are made possible through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. However, the complexity of the hardware and software components within the connected vehicles protocol gives rise to numerous cybersecurity vulnerabilities. One such concern is the potential for Traffic Sybil cyber-attacks, where an attacker creates multiple identities for the same vehicle to interfere with normal operations, posing serious safety and security problems in V2V communications. This praxis introduces a Machine Learning (ML)-based approach to classify and detect these malicious attacks. The study addresses the solution's approaches, limitations, challenges and lessons-learned associated with using ML-based methods.
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