Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach

Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are commonly used for message integrity and authentication of vehicles. However, cryptographic techniques alone may not be sufficient to protect against insider attacks. Many VANET safety applications rely on periodic broadcast of basic safety messages (BSMs) from surrounding vehicles that contain important status information about a vehicle such as its position, speed, and heading. If an attacker (misbehaving vehicle) injects false position information in a BSM, it can lead to serious consequences including traffic congestion or even accidents. Therefore, it is imperative to accurately detect and identify such attackers to ensure safety in the network. This paper presents a novel data-centric approach to detect position falsification attacks, using machine learning (ML) algorithms. Unlike existing techniques, the proposed approach combines information from 2 consecutive BSMs for training and testing. Simulations using the Vehicular Reference Misbehavior (VeReMi) dataset demonstrate that the proposed model clearly outperforms existing approaches for identifying a range of different attack types.

• Record URL:
  http://dx.doi.org/10.1109/OJVT.2021.3138354
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/26441330
• Supplemental Notes:
  • © 2022 Aekta Sharma and Arunita Jaekel.
• Authors:
  • Sharma, Aekta
  • Jaekel, Arunita
  • 0000-0001-6836-9670
• Publication Date: 2022

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1-14
• Serial:
  • IEEE Open Journal of Vehicular Technology
  • Volume: 3
  • Issue Number: 0
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2644-1330
  • Serial URL: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8782711
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Computer security; Detection and identification system applications; Highway safety; Machine learning; Vehicle to vehicle communications; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01837787
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 28 2022 9:42AM