Committee Chair
Reising, Donald
Committee Member
Weerasena, Lakmali; Fadul, Mohamed K. M.
College
College of Engineering and Computer Science
Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
The rapid growth of the Internet of Things (IoT) has connected billions of devices, many with minimal security, making them vulnerable to attacks. Specific Emitter Identification (SEI) offers a passive and reliable security solution by identifying devices through their unique hardware features, enabling serial number level distinction without altering the emitter. SEI can serve as the “something the entity is” factor in zero-trust multi-factor authentication frameworks. However, conventional SEI methods rely on high sampling rates, which are impractical for resource-limited IoT devices. This work evaluates an attention-based SEI model that maintains high identification accuracy at reduced sampling rates. The proposed approach achieves over 97% accuracy using only 2,500 signals sampled at 5 MHz and sustains above 90% accuracy under Rayleigh fading, reducing memory usage by 87.5% without compromising performance. These results highlight the potential of attention mechanisms for efficient, scalable IoT device identification.
Acknowledgments
I dedicate this thesis to my parents, sister, and my grandfather—may his soul rest in peace—who have been my rock and constant source of motivation throughout my life. Your unwavering support and encouragement have been the driving force behind my success. To my parents, Sanna and Ahmed, and my sister, Nouran, I owe you everything. You have always believed in me, even during times when I doubted myself. Your endless love, patience, and sacrifices have given me the strength to pursue my dreams and overcome every obstacle. To my extended family, and friends thank you for your constant encouragement and belief in me. Your support has fueled my determination and given me the courage to follow my passion. To my advisor, Dr. Donald Reising, I am deeply grateful for your guidance, mentorship, and expertise. Your insight and support have been instrumental in shaping my academic and professional growth. I would also like to thank Dr. Fadul and Dr. Tyler for their invaluable contributions, support, and feedback throughout this journey. This thesis is dedicated to all of you—for your unyielding support, and encouragement. I am forever grateful for the sacrifices you have made on my behalf, and I hope this work makes you proud. Thank you for being my strength, my motivation, and my inspiration throughout this journey.
Degree
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Date
12-2025
Subject
IEEE 802.11 (Standard); Internet of Things--Computer networks--Security measures; Pattern recognition systems
Document Type
Masters theses
DCMI Type
Text
Extent
x, 52 leaves
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by-nc-nd/3.0/
Date Available
1-1-2027
Recommended Citation
Mohamedkhir, Mohamedelfateh, "Analysis of signal resampling effects on attention-driven SEI for IoT systems" (2025). Masters Theses and Doctoral Dissertations.
https://scholar.utc.edu/theses/1040
Revised Thesis
Department
Dept. of Engineering