Informatics and Engineering Systems Faculty Publications and Presentations
Document Type
Conference Proceeding
Publication Date
3-18-2024
Abstract
The use of electric motors drive numerous industries via robust mechanical and electromechanical systems. However, their ubiquitous presence across the industry makes them potential targets for cyber-attackers, raising serious privacy concerns. Prior studies have demonstrated the feasibility of accurately fingerprinting various devices using electromagnetic signals. Yet, the specialized equipment required for those methods remain complex and expensive. This study presents MIND-IoT, a novel approach that captures and analyzes the unintended magnetic emissions surrounding electric motors. Our method achieves precise fingerprinting of motors, and determines their operational parameters by leveraging low-cost IoT devices. By focusing solely on magnetic fields, MIND-IoT offers a cost-effective fingerprinting solution while highlighting the urgent need to address associated privacy risks.
Recommended Citation
K. Aboagye-Otchere and J. Castillo, "MIND-IoT: Machine Intelligence and Data-Mining for IoT Threats," 2024 Annual Computer Security Applications Conference Workshops (ACSAC Workshops), Honolulu, HI, USA, 2024, pp. 163-170, https://doi.org/10.1109/ACSACW65225.2024.00024
First Page
163
Last Page
170
Publication Title
2024 Annual Computer Security Applications Conference Workshops (ACSAC Workshops)
DOI
10.1109/ACSACW65225.2024.00024
Comments
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