Date of Award

Fall 12-2022

Document Type


Degree Name

Doctor of Philosophy (PhD)


Electrical & Computer Engineering


Computational Modeling and Systems Engineering

Committee Director

Sachin Shetty

Committee Member

Hong Yang

Committee Member

Chunsheng Xin

Committee Member

Jian Wu


Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack and risk modeling. Others focus on developing intrusion detection and prevention systems mainly applicable for information technology systems without considering the underlying system complexity.

To make the CPSs resilient, it needs a thorough understanding of the currently available security frameworks proposed by standard bodies in this domain. Then one can identify the process to quantify security, risk, and resilience for the CPSs by generating appropriate analytics. This dissertation first proposes a comprehensive cyber resilience framework for industrial control systems (ICS) to address these challenges. We then use the framework to derive system security assessment procedures using the AHP (analytical hierarchy procedure) method. Next, we propose a graph-based model for deriving system critical functionality, critical system components, and system resilience. We then offer a graph-theoretic approach for deriving the operational impacts of the cyberattack on the mission-oriented CPSs. We also leverage artificial intelligence in deriving additional insights from the traditional attack graph-based model to enhance the security and resilience assessment. We propose attack graph embedded machine learning platform for cyber situational awareness and adversarial technique validations methodologies. Overall, this dissertation proposes several insightful methods for handling cybersecurity and resiliency in cyber-physical systems.


In Copyright. URI: This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Copyright, 2022, by Md Ariful Haque, All Rights Reserved.