Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach

• Published in: IEEE open journal of vehicular technology Vol. 3; pp. 1 - 14
• Main Authors: Sharma, Aekta, Jaekel, Arunita
• Format: Journal Article
• Language: English
• Published: IEEE 2022
• Subjects: Classification algorithms; Cryptography; machine learning; Machine learning algorithms; Misbehavior detection; position falsification attack; Random forests; Support vector machines; Vehicle safety; Vehicular ad hoc networks; vehicular ad-hoc network; vehicular communication
• ISSN: 2644-1330; 2644-1330
• DOI: 10.1109/OJVT.2021.3138354

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, cryptograhpic 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.