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Updated: May 7, 2025

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Intrusion detection system for V2X communication in VANET networks using machine learning-based cryptographic

Thiruppathy Kesavan Venkatasamy1, Md Jakir Hossen2, Gopi Ramasamy3,4

  • 1Faculty of Information Technology, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamil Nadu, India.

Scientific Reports
|December 31, 2024
PubMed
Summary

This study introduces machine learning-based cryptographic protocols for intrusion detection (ML-CPIDS) to enhance security in vehicle-to-everything (V2X) communications. The new method offers improved privacy, authentication, and real-time threat detection in vehicular networks.

Keywords:
CryptographyIntrusion detection systemMachine learningV2X communicationVehicular ad-hoc networks

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Vehicle-to-everything (V2X) communication offers significant advantages in fuel efficiency, road safety, and traffic management.
  • However, V2X systems face critical privacy and security challenges, including cyberattacks, data breaches, and unauthorized monitoring.
  • Existing security measures are insufficient to address the evolving threat landscape in vehicular ad hoc networks (VANETs).

Purpose of the Study:

  • To propose and evaluate a novel machine learning-based cryptographic protocol for intrusion detection in V2X communications.
  • To enhance the privacy, security, and integrity of data exchanged within VANETs.
  • To provide real-time threat detection capabilities for V2X networks.

Main Methods:

  • Development of a hybrid approach combining advanced cryptographic protocols with machine learning algorithms.
  • Implementation of robust authentication and encryption techniques for data protection.
  • Utilizing machine learning for real-time identification and mitigation of security threats.

Main Results:

  • The proposed ML-CPIDS demonstrated superior performance in simulations across various VANET environments.
  • Significant improvements were observed in privacy, authentication accuracy, and real-time threat detection capabilities.
  • The system achieved lower latency and enhanced data integrity compared to existing methods.

Conclusions:

  • The ML-CPIDS effectively addresses key privacy and security concerns in V2X communications.
  • This approach offers a robust solution for securing autonomous vehicle networks and traffic management systems.
  • The findings suggest a promising direction for future research in secure V2X communication systems.