International Journal of Science, Engineering and TechnologyAuthors: Dr.Ritu Agarwal, Aryan Sharma, Abhinav Singh
Abstract: The Internet of Vehicles (IoV) is playing a critical role in modern transportation systems, as vehicles interact with each other and roadside infrastructures in real-time. Although this technology can increase road safety and traffic efficiency, it creates various security challenges for IoV. For instance, Sybil, replay, and impersonation attacks can be launched on IoV networks, thereby making them susceptible to attacks. In this paper, we propose a hybrid model that utilizes blockchain tech-nology, SDN, and artificial intelligence-based anomaly detection mechanisms. Specifically, we use random forest to detect abnor-mal behavior of malicious vehicles based on their interactions. Moreover, a modified PBFT (mPBFT) algorithm is employed to improve consensus efficiency by selecting reliable nodes. The proposed model is tested under a simulated IoV envi-ronment and obtains approximately 92detection. In addition, it significantly decreases consensus delay as compared to the conventional PBFT algorithm. Overall, this model has shown good efficiency, scalability, and security in IoV systems.
DOI: http://doi.org/10.5281/zenodo.20280734
