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MSc Thesis Defense Announcement of Smarth Kukreja:"Neural Network based Approach for Detecting Location Spoofing in Vehicular Communication"

Thursday, November 11, 2021 - 10:00 to 12:00

SCHOOL OF COMPUTER SCIENCE 

The School of Computer Science is pleased to present… 

MSc Thesis Defense by: Smarth Kukreja 

 
Date: Thursday November 11th , 2021 
Time:  10:00am – 12:00pm 
Passcode: If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca with suffient notice before the event to obtain the passcode.
 

Abstract:  

Vehicular Ad hoc Network (VANET) is an evolving subset of MANET. It is deployed on the roads, where vehicles act as mobile nodes and communicate with each other as integral components of an Intelligent Transportation System (ITS). Extremely dynamic connections, sensitive data sharing, and time-sensitivity of messages make VANET communications particularly vulnerable to security attacks. An important class of messages shared between the vehicles is the basic safety message (BSM). These messages are broadcasted by each vehicle in the network to report its status to the other vehicles and Road Side Unit (RSU). One common attack is to insert false position information to hamper the roadside safety, leading to road accidents and congestion. Identifying malicious nodes involved in such attacks is crucial to ensure safety in the network. The proposed research presents a neural network-based approach for detecting position falsification attacks in VANET. 

The proposed Deep Learning-based detection of attackers trained and evaluated using the Vehicular Reference Misbehavior Dataset (VeReMi) dataset. The proposed MLP-based model uses resampled single BSM and two consecutive BSM to detect position falsification attacks. 
 
Keywords: Vehicular communication, Misbeaviour detection, Deep Learning 
 

MSc Thesis Committee:  

Internal Reader: Dr. Kalyani Selvarajah 
External Reader: Dr. Animesh Sarker 
Advisor: Dr. Arunita Jaekel 
Chair: Dr. Scott Goodwin 
 
Vector Institute in Artificial Intelligence, artificial intelligence approved topic logo                                                                                                            
 
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