Committee Chair

Reising, Donald R.

Committee Member

Loveless, Thomas D.; Fadul, Mohamed K. M.

Department

Dept. of Electrical Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

The Internet of Things (IoT) is projected to reach 30.9 billion devices by 2025. However, most lack adequate security measures against sophisticated threats. Specific Emitter Identification (SEI) is a crucial security approach for authenticating wireless emitters. This work integrates RF-DNA fingerprinting, a specialized form of SEI, with Deep Learning (DL) techniques to authenticate the identity of authorized emitters. This authentication becomes crucial in the presence of “rogue” emitters who deliberately impersonate authorized emitters using falsified digital credentials. The RF-DNA fingerprints are extracted from the entropy-selected regions within the TF representations of an emitter’s signals. The obtained results demonstrate the success of a Convolutional Neural Network (CNN) in verifying the identities of all authorized emitters at an accuracy rate of 95% or higher. Additionally, the CNN effectively detects and rejects all twelve rogue attacks with an accuracy rate of 89% or better, at an SNR of 9 dB.

Acknowledgments

I would like to express my heartfelt gratitude to my parents for their unwavering support, encouragement, and belief in my abilities throughout this journey. Your constant presence and love have been my guiding light. To my family and friends, your encouragement and understanding have been invaluable to me. Your belief in my aspirations has motivated me to persevere and reach this significant milestone. I am immensely thankful to my supervisor, Dr. Reising, for your guidance, expertise, and unwavering commitment to my academic growth. Your mentorship has been instrumental in shaping the direction of my research and enhancing my skills. With profound appreciation to all who have played a part in my academic journey, thank you for being by my side and contributing to this achievement.

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

5-2024

Subject

Biometric identification--Computer networks--Security measures; Deep learning (Machine learning); Entropy (Information theory); Pattern recognition systems; Radio frequency identification systems--Access control

Keyword

Deep Learning; Time-Frequency Representation; Specific Emitter Identification; ID-verification; Rogue Rejection; Entropy

Document Type

Masters theses

DCMI Type

Text

Extent

ix, 48 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

Date Available

5-31-2025

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Available for download on Saturday, May 31, 2025

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