Biblio

Vehicular Ad-Hoc Network (VANET) is a subcategory of Intelligent Transportation Systems (ITS) that allows vehicles to communicate with other vehicles and static roadside infrastructure. However, the integration of cyber and physical systems introduce many possible points of attack that make VANET vulnerable to cyber attacks. In this paper, we implemented a machine learning-based intrusion detection system that identifies False Data Injection (FDI) attacks on a vehicular network. A co-simulation framework between MATLAB and NS-3 is used to simulate the system. The intrusion detection system is installed in every vehicle and processes the information obtained from the packets sent by other vehicles. The packet is classified into either trusted or malicious using Support Vector Machines (SVM). The comparison of the performance of the system is evaluated in different scenarios using the following metrics: classification rate, attack detection rate, false positive rate, and detection speed. Simulation results show that the SVM-based IDS is able to provide high accuracy detection, low false positive rate, consequently improving the traffic congestion in the simulated highway.