Theses and Dissertations

Date of Award

8-1-2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Jorge Castillo

Second Advisor

Timothy Wylie

Third Advisor

Erik Enriquez

Abstract

Electric motors are vital to industry, transport, and energy, yet their maintenance challenges persist. While traditional reactive maintenance leads to costly downtime and safety risks, predictive maintenance, especially through IoT and machine learning offers early fault detection and operational efficiency. However, this shift introduces security concerns due to unintended magnetic emissions from motors. These emissions, though useful for non-intrusive monitoring, can be exploited to eavesdrop on sensitive industrial processes. This dissertation explores the dual nature of magnetic emissions: their value in motor diagnostics and their potential as a security vulnerability. It demonstrates how emissions can identify motors, monitor health, and detect malfunctions without direct contact. Simultaneously, it reveals how adversaries might leverage this data to infer confidential information, like manufacturing patterns or schedules. The research centers on evaluating monitoring and fingerprinting techniques and quantifying their efficiency and risks. By analyzing both maintenance benefits and adversarial threats, the study contributes to predictive maintenance and industrial cybersecurity. It ultimately urges industries to balance technological advances with data protection, ensuring reliability does not compromise security.

Comments

Copyright 2025 Kwabena Aboagye-Otchere. All Rights Reserved. https://proquest.com/docview/3275322623

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