Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

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.

arXiv commentPublished in IEEE Global Communications Conference (GLOBECOM) 2019; Code is available at Github link: https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning

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• SMOTE: Synthetic Minority Over-sampling Technique
• GIDS: GAN based Intrusion Detection System for In-Vehicle Network
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection
• DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach

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