Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

TL;DR

This paper proposes a tree-structure machine learning-based intrusion detection system for Internet of Vehicles, effectively identifying cyber-attacks with high accuracy and low computational cost, enhancing the security of autonomous vehicle networks.

Contribution

It introduces an ensemble learning and feature selection approach for a tree-based IDS tailored for IoV, improving detection performance and efficiency.

Findings

High detection rate for various cyber-attacks

Low computational cost achieved

Effective identification of attacks in AV networks

Abstract

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.