Biblio

Security of Internet of vehicles (IoV) is critical as it promises to provide with safer and secure driving. IoV relies on VANETs which is based on V2V (Vehicle to Vehicle) communication. The vehicles are integrated with various sensors and embedded systems allowing them to gather data related to the situation on the road. The collected data can be information associated with a car accident, the congested highway ahead, parked car, etc. This information exchanged with other neighboring vehicles on the road to promote safe driving. IoV networks are vulnerable to various security attacks. The V2V communication comprises specific vulnerabilities which can be manipulated by attackers to compromise the whole network. In this paper, we concentrate on intrusion detection in IoV and propose a multilayer perceptron (MLP) neural network to detect intruders or attackers on an IoV network. Results are in the form of prediction, classification reports, and confusion matrix. A thorough simulation study demonstrates the effectiveness of the new MLP-based intrusion detection system.

The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation.