The School of Computer Science is pleased to present…
Misbehavior Detection in Vehicular Networks Using Unsupervised Algorithms
MSc Thesis Proposal by: Girish Nathi
Date: Friday, March 8th, 2024
Time: 10:00 am to 11:30 am
Location: Essex Hall Room: 122
Abstract:
A wireless network connects a collection of moving or stationary automobiles and other infrastructure nodes to form a vehicular ad hoc network or VANET. One of the primary functions of VANETs is to ensure drivers' comfort and safety in moving vehicles. Vehicles in VANET share safety and non-safety information through periodic broadcasts of Basic Safety Messages (BSM), which contain the vehicle's relevant status information, such as position, heading, speed, etc. BSMs are used for safety applications such as collision avoidance and inserting false information in BSMs can have serious consequences. An attack known as position falsification occurs when a vehicle broadcasts a fictitious BSM position, which can cause traffic jams or even accidents and such attacks need to be detected quickly and accurately. Several supervised learning algorithms have been proposed for detecting such attacks. However, it is difficult to obtain labelled datasets in “real-world” scenarios, so there is a need to design suitable unsupervised ML models. In this thesis, we plan to develop unsupervised ML models for detecting position falsification. The effectiveness of the proposed models will be evaluated by comparing with existing detection techniques using the publicly available Vehicular Reference Misbehavior (VeReMi) dataset, based on simulated traffic data.
Thesis Committee:
Internal Reader: Dr. Muhammad Asaduzzaman
External Reader: Dr. Ahmed Hamdi Sakr
Advisor: Dr. Arunita Jaekel
