Committee Chair

Reising, Donald R.

Committee Member

Loveless, Thomas Daniel; Sartipi, Mina; Fadul, Mohamed

Department

Dept. of Computational Science

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Since its introduction into society in the late 1800’s, electricity has become a critical component in how society has functioned. High-voltage electricity provides power for appliances that have become integral to daily living, such as lights, refrigeration, and Heating, Ventilation, And Cooling (HVAC). Low-voltage electricity is used to process and transmit information on and between computers. This information may pertain to medical, financial, and defense-related activities. Over the past hundred years, significant research has been performed to improve electrical-based technology’s capability, scale, resiliency, and security. This study focuses solely on resiliency and security, both of which require efficient data collection, storage, and processing. In the case of the resiliency of high-voltage electrical transmission, the continuous, 24-hour collection of transmission line activity generates data that exceeds the ability to store for long-term forensics. For the secure transmission of information, the information must be (i) hard to recover by an adversary and (ii) trusted by the recipient. This dissertation presents research aimed at (i) improving the reliability of High-voltage electrical transmission by statistically compressing data at the edge by up to 99.96% while still maintaining actionable information to enable real-time Incipient Fault Prediction (IFP), (ii) reducing an adversary’s ability to intercept information by introducing AI-based, session-based cryptographic scheme generation, and (iii) improving information’s trust by identifying the source of wireless transmission at the physical layer by enabling cross-collection Specific Emitter Identification (SEI) at up to 99.51% blind collection accuracy across eight commercial emitters.

Acknowledgments

I would like to thank all of my committee chair, Dr. Donald R. Reising, and all committee members, Dr. T. Daniel Loveless, Dr. Mina Sartipi, and Dr. Mohamed Fadul for their guidance over the creation of this dissertation.

Degree

Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy.

Date

12-2025

Subject

Artificial intelligence; Data encryption (Computer science); Electric power distribution; Information warfare; Machine learning

Keyword

Machine Learning; Artificial Intelligence; Specific Emitter Identification; RF Fingerprinting; Electronic Warfare; Spectral Warfare; Cross-Collection; Multi-Domain; Adversarial Nerual Cryptography; Encryption; Neural Network; One-Time Pad; Perfect Forward Security; Power Grid; Power Distribution; Critical Infrastructure; Electrical Infrastructure; Power Signal Histograms; Incipient Fault Prediction

Document Type

Doctoral dissertations

DCMI Type

Text

Extent

xxix, 202 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

Date Available

8-31-2026

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Available for download on Monday, August 31, 2026

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