Tuesday, May 10, 2022 - 09:30 to 11:00
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present…
MSc Thesis Proposal by: Bhuiyan Mustafa Tawheed
Date: Tuesday May 10th 2022
Time: 9.30am – 11.00am
Meeting URL: https://us06web.zoom.us/j/87351798367?from=addon
Passcode: If interested in attending this event, contact the Graduate Secretary at firstname.lastname@example.org with sufficient notice before the event to obtain the passcode.
An integral component of the Intelligent Transportation System (ITS) is the emerging technology called Vehicular ad-hoc network (VANET). VANET allows Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication wirelessly to improve road safety, traffic congestion, and information dissemination. Communication of vehicles in a VANET network is vulnerable to various attacks. Commonly used cryptographic techniques alone are insufficient to ensure and protect the message integrity and authentication of vehicles from insider attacks. In such cases, additional measures are necessary to ensure the correctness of the transmitted data. Each vehicle in the network periodically broadcasts a basic safety message (BSM) that contains essential status information about a vehicle, such as its position, speed, and heading to other vehicles and Road Side Units (RSU) to report its status. A speed offset attack is where an attacker (misbehaving vehicle) misleads the network by adding an offset value to its actual speed data in each BSM. Such attacks can result in traffic congestion and road accidents, and therefore, it is essential to accurately detect and identify such attackers to ensure safety in the network. This research proposes a novel data-centric approach for detecting speed offset attacks using Machine Learning (ML) algorithms. Vehicular Reference Misbehavior (VeReMi) Extension Dataset is used for this research. Preliminary results indicate that the proposed model is able to detect malicious nodes in the network more accurately than existing techniques.
Keywords: VANET, Speed Offset attacks, Machine Learning algorithms, Misbehavior detection, Classification algorithms
MSc Thesis Committee:
Internal Reader: Dr. Saeed Samet
External Reader: Dr. Esam Abdel-Raheem
Advisor: Dr. Arunita Jaekel
MSc Thesis Proposal Announcement
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