The Internet of Vehicles (IoV) is a branch of the Internet of Things that deals with vehicle-to-vehicle communication and intelligent transport systems (ITS). But this connection is not without consequences, because the more a system exchanges information, the more vulnerable it is to various attacks from malicious actors. (hackers). Vehicle Internet security is a great challenge that security professionals face every day. Moreover, despite the deployment of diverse technologies by smart cities to obtain varied, high-performance cloud services, security concerns continue to appear in communications entities that share information. In this article, an intrusion detection system (IDS) based on machine learning is proposed to improve safety in vehicle Internet systems (IoV). The IDS uses random forest (RF) algorithms, decision Tree, Adaboost, and gradient boost on an IoV traffic data set. The hyperparameters of machine learning models are optimized using a meta-heuristic optimization algorithm called the CDO (Chemotactic Differential Evolution) algorithm. The proposed IDS achieved high performance in terms of up to 99.91% accuracy for the Adaboost algorithm in the binary case and 9.81% accuracy in the case of the decimal dataset. High performance of precision, recall, and F1 score were also observed in this study. The optimization has significantly improved the performance of the models by optimizing their hyperparameters. The study was conducted using data sets built by CICIS (Canadian Institute of Cybersecurity) with a real vehicle to evaluate the proposed detection system. The experimental results show that the proposed IDS has significantly higher detection performance.

Keywords - Internet of Vehicle, Cybersecurity, Intrusion Detection System, Machine learning

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