Intrusion Detection in Internet of Vehicles Using Machine Learning

Hop Le, Izzat Alsmadi
12/16/2025
cs.CRcs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

The Internet of Vehicles (IoV) has evolved modern transportation through enhanced connectivity and intelligent systems. However, this increased connectivity introduces critical vulnerabilities, making vehicles susceptible to cyber-attacks such Denial-ofService (DoS) and message spoofing. This project aims to develop a machine learning-based intrusion detection system to classify malicious Controller Area network (CAN) bus traffic using the CiCIoV2024 benchmark dataset. We analyzed various attack patterns including DoS and spoofing attacks targeting critical vehicle parameters such as Spoofing-GAS - gas pedal position, Spoofing-RPM, Spoofing-Speed, and Spoofing-Steering\_Wheel. Our initial findings confirm a multi-class classification problem with a clear structural difference between attack types and benign data, providing a strong foundation for machine learning models.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

87% match2025

AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)

87% match1 repo112024

One-Class Classification for Intrusion Detection on Vehicular Networks

86% match2023

Evaluating False Alarm and Missing Attacks in CAN IDS

86% match2026

Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN

84% match2024

Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation

84% match2025

Cite this paper

@article{le2025intrusion,
  title  = {Intrusion Detection in Internet of Vehicles Using Machine Learning},
  author = {Hop Le and Izzat Alsmadi},
  year   = {2025},
  eprint = {2512.14958},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.14958v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2512.14958.svg)](https://codeofpaper.com/paper/2512.14958)

Discussion