Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

Abstract--The use of autonomous vehicles (A Vs) is a promising technology in Intelligent Transportation Systems (ITSs) t o improve safety and driving efficiency. V ehicle-to-everythin g (V2X) technology enables communication among vehicles and other infrastructures. However, A Vs and Internet of V ehicles (Io V) are vulnerable to different types of cyber-attacks such as d enial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed b ased on tree-structure machine learning models. The results fro m the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability t o identify various cyber-attacks in the A V networks. Further more, the proposed ensemble learning and feature selection appro aches enable the proposed system to achieve high detection rate an d low computational cost simultaneously. With more vehicles, devices, and infrastructures involved, the conventional vehicular ad hoc networks (V ANETs) are gradually evolving into the Internet of V ehicles (IoV) [1].