The UTC Graduate School is pleased to announce that Joshua Tyler will present Doctoral research titled, Introducing Statistical and Machine Learning-based Methods of Enhancing the Resiliency and Security of Electrical-based Critical Infrastructure on 03/25/2025 at 3:00PM in MDRB Auditorium. Everyone is invited to attend. 

Computational Science

Chair: Donald R. Reising

Co-Chair: 

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 normal day-to-day living, such as lights, refrigeration, and Heat, Ventilation, and Air Conditioning (HVAC), etc. Low-voltage electricity is used for the processing and transmission of information on and in-between computers. This information may pertain to medical, financial, and defense-related activities. Over the past hundred years, there has been significant research effort spent in improving the capability, scale, resiliency, and security of electrical-based technology. This study focuses solely on resiliency and security; both of which require the efficient collection, storage, and processing of data. In the case of 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. The work in this study is presented to: (i) improve reliability of High-voltage electrical transmission by statistically compress data at the edge by up to 98.5% while still maintaining actionable information, (ii) reduce an adversary's ability to intercept information by introducing AI-based, session-based cryptographic scheme generation, and (iii) improve information's trust by identifying the source of a 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.