The School of Computer Science is pleased to present…
Date: Friday, May 9th,2025
Time: 11:00 am
Location: Essex Hall, Room 186
The main goal of Cooperative Intelligent Transport Systems (C-ITS) is to enhance road safety through real-time data exchange between vehicles via Vehicle-to-Everything (V2X) communication. The connectivity between vehicles through V2X communication creates security risks because attackers can easily falsify position and speed data, which leads to vehicle misdirection and traffic system disruption. When operating in dynamic environments, existing rule-based and cryptographic detection systems lack sufficient adaptability and scalability. To address these challenges, this thesis presents supervised machine learning as a solution to detect misbehaviour in Collective Perception Services (CPS). The research uses the SimCPS dataset to simulate six attack types that target position and speed information across different attacker densities and target strategies.
The research presents a detection framework which analyzes collective perception messages to detect malicious activities effectively. Rather than relying on predefined rules, the model learns attack patterns from the data itself instead of using predefined rules, which enables it to handle various scenarios. The results demonstrate strong detection performance, particularly in identifying subtle manipulations that traditional methods often overlook.
Internal Reader: Dr. Xiaobu Yuan
External Reader: Dr. Maher Azzouz
Advisor: Dr. Arunita Jaekel
Chair: Dr.Asish Mukhopadhyay
