Skip to main content
Loading Events

« All Events

Graduate Defense: Chibuzo Ukegbu

June 10 @ 10:00 am - 12:00 pm

Dissertation Defense

Dissertation Information

Title: A Framework For Safety And Security Violation Detection In Industrial Control Systems Using Cooperative Detection Techniques

Program: Doctor of Philosophy in Computing

Advisor: Dr. Hoda Mehrpouyan, Computer Science

Committee Members: Dr. Nasir Eisty, Computer Science, and Dr. Jyh-haw Yeh, Computer Science

Abstract

The resilience of critical infrastructures, such as water and chemical processing plants, is a paramount concern in national security. These facilities are increasingly targeted by sophisticated adversaries, including Advanced Persistent Threat (APT) groups, highlighting the necessity for enhanced protective measures. This dissertation, which addresses the urgent need to rethink current security paradigms, could potentially revolutionize your approach to security by developing advanced techniques for detecting safety and security violations within industrial control systems.

Focusing on these industrial environments’ unique process logic and chemical process dynamics, this research goes beyond conventional detection systems, offering a fresh perspective. This work pioneers a cooperative verification framework by integrating formal verification methods, Descriptive Logic ontologies, and dynamic modeling of control equations. This approach not only improves the detection of anomalies but also elucidates the underlying relationships and root causes of such violations through comprehensive ontological mappings, opening up new avenues for research and innovation.

The dissertation is structured into two main sections: safety detection and security detection. For safety, the research employs a combination of formal verification, modeling, and simulation techniques tailored to the operational specifics of processing plants. In the security domain, the study introduces a novel hybrid setpoint buffer technique combined with an Ensemble Method Machine Learning approach. This dual strategy is designed to enhance the robustness of security measures.

Empirical results from this study indicate a significant enhancement in the detection capabilities over existing methods. These new techniques, while not intended to replace current systems, offer a practical and effective way to enhance the security of critical infrastructures. They should be integrated within a broader defense-in-depth strategy to ensure comprehensive protection. This research contributes to the field by offering a more nuanced understanding of safety and security violation detection in industrial settings, proposing a model for future innovations in the area.