Mississippi State University
Ball, John E.
Fowler, James E.
Date of Degree
Original embargo terms
Graduate Thesis - Open Access
Master of Science
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
Wireless networks are vulnerable to adversarial devices by spoofing the digital identity of valid wireless devices, allowing unauthorized devices access to the network. Instead of validating devices based on their digital identity, it is possible to use their unique "physical fingerprint" caused by changes in the signal due to deviations in wireless hardware. In this thesis, the physical fingerprint was validated by performing classification with complex-valued neural networks (NN), achieving a high level of accuracy in the process. Additionally, zero-shot learning (ZSL) was implemented to learn discriminant features to separate legitimate from unauthorized devices using outlier detection and then further separate every unauthorized device into their own cluster. This approach allows 42\% of unauthorized devices to be identified as unauthorized and correctly clustered
National Institute of Justice grant 2018-75-CX-K002
Smith, Logan, "Machine learning for wireless signal learning" (2021). Theses and Dissertations. 5147.