A misbehavior detection framework for cooperative intelligent transport systems

Abstract

With changing times, the need for security increases in all fields, whether we talk about cloud networks or vehicular networks. In every place, it has its importance, but in vehicular networks where the lives of human beings are involved, security becomes the topmost priority. Therefore, this article aims to shed light on Misbehavior Detection Framework (MDF) used in the Cooperative Intelligent Transport Systems community. Here, MDF keeps an eye on malicious entities on the roads. It is done by regularly evaluating two main checks: consistency and local plausibility. These checks are done by Intelligent Transport System Stations. All the messages received through Vehicle-to-Everything are scrutinized through this model. After that, all the messages are evaluated by local detection mechanisms to decide the holistic message's plausibility. This article mainly focuses on the logic behind the proposed Misbehavior Detection Framework providing more security, evaluating various Machine Learning-based models to ensure one best out of all based on quality and computation latency of all models along with the results of various parameters, such as Recall, Precision, F1 Score, Accuracy, Bookmaker Informedness, Markedness, Mathews Correlation Coefficient, Kappa, and achieved the best results.With changing times, the need for security increases in all fields, whether we talk about cloud networks or vehicular networks. In every place, it has its importance, but in vehicular networks where the lives of human beings are involved, security becomes the topmost priority. Therefore, this article aims to shed light on Misbehavior Detection Framework (MDF) used in the Cooperative Intelligent Transport Systems community. Here, MDF keeps an eye on malicious entities on the roads. It is done by regularly evaluating two main checks: consistency and local plausibility. These checks are done by Intelligent Transport System Stations. All the messages received through Vehicle-to-Everything are scrutinized through this model. After that, all the messages are evaluated by local detection mechanisms to decide the holistic message's plausibility. This article mainly focuses on the logic behind the proposed Misbehavior Detection Framework providing more security, evaluating various Machine Learning-based models to ensure one best out of all based on quality and computation latency of all models along with the results of various parameters, such as Recall, Precision, F1 Score, Accuracy, Bookmaker Informedness, Markedness, Mathews Correlation Coefficient, Kappa, and achieved the best results.