Date of Award
May 2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Civil Engineering
Committee Member
Mashrur Chowdhury
Committee Member
Amy Apon
Committee Member
Feng Luo
Committee Member
Long Cheng
Abstract
The transportation system is rapidly evolving with new connected and automated vehicle (CAV) technologies that integrate CAVs with other vehicles and roadside infrastructure to form a transportation cyber-physical system (TCPS). Through connectivity, CAVs affect their environments and vice versa, increasing the size of the cyberattack surface and the risk of exploitation of security vulnerabilities by malicious actors. Thus, a greater understanding of potential CAV-TCPS cyber-attacks and of ways to prevent them is a high priority. Moreover, making the CAV navigate safely in an unexpected environment is a critical safety requirement. Considering the safety while maintaining the in-vehicle security is the focus of this study, where first, in part 1, the author explores the CAV safety through machine learning models, more specifically deep neural network, to help the vehicle to navigate safely in an unexpected environment, which is required for real-world deployment and has not been fully explored by researchers and industries. In part 2, the author developed a connected vehicle application development platform (CVDeP), such that developers can develop and validate the CAV safety and mobility applications in a controlled and real-world connected vehicle testbed. Our study shows that applications developed through the platform meet the safety requirements of connected vehicle applications. Later, in part 3, the author explores the in-vehicle security aspect, where the author leverages the state-of-the-art cloud supported quantum computers to classify in-vehicle cyberattacks, more specifically amplitude shift attacks. The author develop the quantum-classical hybrid neural network to detect amplitude shift in-vehicle cyberattack. This study integrates the digital infrastructure and a CAV’s in-vehicle system, where the author has shown the potential of using a combination of quantum and classical neural network to improve the cyberattack detection accuracy compared to classical neural network and quantum neural network alone.
Recommended Citation
Islam, Md Mhafuzul, "Improving the Security and Safety of Transportation Cyber-Physical Systems" (2021). All Dissertations. 2809.
https://tigerprints.clemson.edu/all_dissertations/2809