Publication Date
2024
Document Type
Thesis
Committee Members
Fathi Amsaad, Ph.D. (Advisor); Junjie Zhang, Ph.D. (Committee Member); Huaining Cheng, Ph.D. (Committee Member); Nitin Pundir, Ph.D. (Committee Member); Thomas Wischgoll, Ph.D. (Other); Subhashini Ganapathy, Ph.D. (Other)
Degree Name
Master of Science (MS)
Abstract
In today's technological landscape, hardware devices are integral to critical applications such as industrial automation, autonomous vehicles, and medical equipment, relying on advanced platforms like FPGAs for core functionalities. However, the multi-stage manufacturing process, often distributed across various foundries, introduces substantial security risks, notably the potential for hardware Trojan insertion. These malicious modifications compromise the reliability and safety of hardware systems. This research addresses the detection of hardware Trojans through side-channel analysis, utilizing power and electromagnetic signal data, combined with meta-learning techniques, specifically model stacking. By employing diverse base models and a meta-model to consolidate predictions, this non-invasive approach effectively identifies Trojans without requiring direct access to internal circuitry. The methodology demonstrates robust classification capabilities, achieving an accuracy of 88.0%, precision of 81.0%, and recall of 95.0%, even on previously unseen data. The results highlight the superior performance of meta-learning over traditional detection methods, offering an efficient and reliable solution to enhance hardware security.
Page Count
125
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2024
Copyright
Copyright 2024, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
