Machine learning algorithms for intruder signal detection and device localization in wireless radio frequency systems
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Koppisetti, Naga Raghavendra Surya Vara Prasad
issued: 2021-09-10T21:13:45Z
issued: 2021
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Radio frequency (RF) wireless systems generate large amounts of data every day on the signal content and the received signal strength (RSS) information. However, limited efforts have been put into analyzing the RF data for security applications, such as the detection of unauthorized transmissions from an intruder. To bridge this gap, the Ph.D. thesis presents machine learning algorithms which detect and localize the intruder devices from their RF transmissions. First, we study the problem of detecting the intruder RF signals in wideband RF traces. An example setup is considered, with the intruder transmitting Wi-Fi signals and there exists interference from Bluetooth and microwave oven signals. We show that the conventional energy-thresholding algorithms are sensitive to noise variations and they require handcrafted parameters for each RF trace. To address this concern, we develop a deep learning solution, which employs convolutional neural networks to perform the signal de- tection. Experiments on both synthetic and real RF traces confirm the superior performance of the proposed solution in terms of the achieved mean average precision. Second, we study the problem of locating the intruder device from the RSS measured passively in the system. We work with the difference of RSS (DRSS), calculated with respect to a reference sensor, in order to handle the unknown transmission power and device heterogeneity of the intruder. The localization problem is formulated as a Gaussian process regression (GP) task to obtain the location estimates and the associated confidence intervals in closed-form. We propose two GP methods which take the stochastic nature of the test DRSS into account and provide more accurate confidence intervals on the test locations than the conventional GP method. Third, to improve the localization accuracy of the proposed GP methods, we present data reconstruction techniques which exploit the low-dimensionality exhibited by the DRSS vectors. Fourth, we …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/79700
note: Machine Learning Algorithms for IntruderSignal Detection and Device Localization inWireless Radio Frequency SystemsbyNaga Raghavendra Surya Vara Prasad KoppisettiB.Tech., Indian Institute of Technology Bhubaneswar, India, June 2012M.A.Sc., The University of British Columbia, Canada, May 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Electrical and Computer Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)September 2021c© Naga Raghavendra Surya Vara Prasad Koppisetti 2021The following individuals certify that they have read, and recommend to the Faculty of Graduate and Post-doctoral Studies for acceptance, the dissertation entitled:Machine Learning Algorithms for Intruder Signal Detection and Device Localization in Wireless RadioFrequency Systems,submitted by Naga Raghavendra Surya Vara Prasad Koppisetti, in partial fulfillment of the requirements forthe degree of Doctor of Philosophy in Electrical and Computer EngineeringExamining Committee:Vijay K. Bhargava, Professor, Electrical and Computer Engineering, UBC VancouverSupervisorVincent Wong, Professor, Electrical and Computer Engineering, UBC VancouverSupervisory Committee MemberRyozo Nagamune, Associate Professor, Mechanical Engineering, UBC VancouverUniversity ExaminerVictor Leung, Professor Emeritus, Electrical and Computer Engineering, UBC VancouverUniversity ExaminerOctavia Dobre, Professor, Electrical and Computer Engineering, Memorial UniversityExternal ExaminerAdditional Supervisory Committee Members:Sudip Sekhar, Associate Professor, Electrical and Computer Engineering, UBC VancouverSupervisory Committee MemberLutz Lampe, Professor, Electrical and Computer Engineering, UBC VancouverSupervisory Committee MemberiiAbstractRadio frequency (RF) wireless systems generate large amounts of data every day on the signal content andthe received signal strength (RSS) information. However, limited efforts h…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0401966
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Bhargava, Vijay K., 1948-
type: Text
identifier URI: http://hdl.handle.net/2429/79700