MSc Thesis Defense by: Sarthak Khanduja

The School of Computer Science is pleased to present…

Traffic Congestion Sybil Attack Detection in VANETs using Machine Learning Techniques

MSc Thesis Defense by: Sarthak Khanduja

 

Date: Friday, 26 April 2024

Time:  1:00 PM

Location: Dillion Hall, Room 255

 

Abstract:

Integrating Vehicular Ad-Hoc Networks (VANETs) into modern Intelligent Transportation Systems (ITS) introduces critical security concerns. This research addresses the emerging threat of Traffic Congestion Sybil Attacks, where malicious entities inject spurious data to fabricate artificial traffic congestion. The methodology involves a detailed examination of the VeReMi dataset, a benchmark for VANET research, coupled with state-of-the-art classification machine learning algorithms. The analysis includes training and evaluating these models to identify patterns indicative of Grid Sybil attacks. Preliminary results obtained through meticulous testing demonstrate a substantial enhancement in various classification metrics, showcasing promising improvements, especially in enhancing the recall value for accurately identifying maliciously induced traffic congestions. These initial findings underscore the potential for further refinements and heightened classification metrics in subsequent phases of the research. This thesis emphasizes the urgency of securing VANETs against Traffic Congestion Sybil Attacks, presenting an innovative solution through the fusion of machine learning techniques and the VeReMi dataset. The outcomes contribute to theoretical understanding and hold practical implications for enhancing the security of vehicular communication networks.

Keywords: VANETs, Machine Learning, VeReMi Dataset, Sybil Attack, ITS