The School of Computer Science is pleased to present…

Traffic Congestion Sybil Attack Detection in VANETs using Machine Learning Techniques

MSc Thesis Proposal by: Sarthak Khanduja

 

Date: Friday January 5, 2024

Time:  12:00 PM – 1:30 PM

Location: Essex Hall, Room 122

 

Abstract:

The integration of Vehicular Ad-Hoc Networks (VANETs) into modern Intelligent Transportation Systems (ITS) introduces critical security concerns. This research aims to address the emerging threat of Traffic Congestion Sybil Attacks, where malicious entities inject spurious data to fabricate artificial traffic congestions. 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 the training and evaluation of 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 not only contribute to theoretical understanding but also hold practical implications for enhancing the security of vehicular communication networks.

 

Thesis Committee:

Internal Reader: Dr. Dan Wu

External Reader: Dr. Jagdish Pathak

Advisor: Dr. Arunita Jaekel