Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Ball, John E.

Committee Member

Tang, Bo

Committee Member

Young, Maxwell

Committee Member

Fowler, James E.

Date of Degree

4-30-2021

Original embargo terms

Worldwide

Document Type

Graduate Thesis - Open Access

Major

Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

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

Sponsorship

National Institute of Justice grant 2018-75-CX-K002

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