Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
This addresses security vulnerabilities in autonomous vehicles and IoV networks, but it is incremental as it applies existing tree-based methods to this domain.
The paper tackles cyber-attack detection in Internet of Vehicles using tree-based machine learning models, achieving high detection rates and low computational cost through ensemble learning and feature selection.
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.