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Identifying Units on a WiFi Based on Their Physical Properties
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This project aims to classify different units on a wireless network with the use of their frequency response. This is in purpose to increase security when communicating over WiFi. We use a convolution neural network for finding symmetries in the frequency responses recorded from two different units. We used two pre-recorded sets of data which contained the same units but from two different locations. The project achieve an accuracy of 99.987%, with a 5 hidden layers CNN, when training and testing on one dataset. When training the neural network on one set and testing it on a second set, we achieve results below 54.12% for identifying the units. At the end we conclude that the amount of data needed, for achieving high enough accuracy, is to large for this method to be a practical solution for non-stationary units.

Place, publisher, year, edition, pages
2019. , p. 8
Series
TRITA-EECS-EX ; 2019:139
Keywords [en]
Convolutional Neural Network, Supervised Learning, WiFi, Security, Activation Functions, Validation, WiFi, Datasets
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-254255OAI: oai:DiVA.org:kth-254255DiVA, id: diva2:1329947
Subject / course
Electrical Engineering
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Examiners
Available from: 2019-06-25 Created: 2019-06-25 Last updated: 2019-06-25Bibliographically approved

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CiteExportLink to record
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  • apa
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Output format
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