PhD Projects

Completed PhD Projects

Towards a Systematic Security Evaluation of the Automotive Bluetooth

Student: Madeline Cheah
Project Summary:
• Framework for systematic evaluation
• Dataset for automotive Bluetooth threat intelligence
• Proof-of-concept software tooling
• Classification of evidence in a security assurance case
• Methodology for formalisation of empirical penetration testing

Systematic Vehicle Evaluation

Student: Milena Kukova
Project Summary:
• Model modelling relationship between vehicles, their attributes, customer responses and influences on those
• New customer journey model
• Comprehensive list of vehicle attributes and a method for calculating attribute importance
• More accurate vehicle classifications

Novel Hybrid Methodology for Structural Optimisation

Student: Alexis Wilson
Project Summary:
• Evaluation of commercial topology optimisation algorithms
• `Hybrid Option Parameters’ implemented in Microsoft Powershell
• Demonstration of advantages of simultaneous linear and non-linear optimisation

A CAN Fuzz Testing Methodology for Automotive Security

Student: Daniel Fowler
Project Summary:
A fuzz testing methodology to increase automotive cyber-security resilience:- The increasing number of connected computational components in a vehicle enlarges the attack surface for malicious agents. To ensure a degree of resilience against remote attacks, manufacturers must perform cyber-security tests and audits. SAE J3061 regards fuzz testing as part of the testing regime, yet there little is available on how to apply fuzz testing to vehicles. This research addresses the application of fuzz testing to Controller Areas Networks and attached components. 
• Prototype automated CAN fuzzing tool implemented in C
• Demonstration of fuzz testing and its effectiveness for automotive cybersecurity
• Methodology for addressing combinatorial explosion in fuzz testing
• Method of attack using configuration variation of ECU bitrates

Ride Comfort Evaluation

Student: Maciej Cieslak
Project Summary:
• Artificial Neural Network based model for ride comfort evaluation along with the verification and validation of
the model
• Demonstration of feasibility of using biometric measurement for ride comfort studies

First and Last Mile Mobility with Human-centred Design

Student: Joscha Wasser
Project Summary:
• Key design factors for first and last mile mobility vehicles with design recommendations
• Novel comfort model
• Benchmark of approaches and design philosophies of first and last mile vehicles
• MiCAR concept and design
• Mixed-reality simulation