A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles

The rapid advancement of communication is introducing a new era for the Internet of Vehicles (IoV) in the context of Smart Cities. Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intrusion Detection. This paper presents a robust machine learning (ML) technique to enhance the security of IoV networks by developing an efficient intrusion detection system (IDS). In this paper, we proposed a fine tree-based model to study the complex behavior of network traffic inside the IoV to detect and classify anomalies for securing the IoV. The proposed fine tree-based model can be validated by conducting extensive experiments with benchmark real-world datasets which can simulate emerging IoV scenarios. The proposed Fine Tree-based IDS model, along with other models, has been evaluated using metrics such as mean accuracy, precision, recall, F1-score, specificity and error rate. The proposed model outperformed the others across each metric, achieving near-perfect results with a mean accuracy, precision, recall, F1-score, and specificity of 0.99999. However, the other models achieved mean values ranging from 0.90 to 0.98 across these metrics. Additionally, the proposed model achieved an exceptionally low mean error rate of 0.00001, while the error rates of the other models ranged from 0.02 to 0.05. The experimental findings demonstrate the superior performance of the proposed model in detecting and classifying intrusions within IoV.