ResearchSpace

Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting

Show simple item record

dc.contributor.author Otto, AJ
dc.contributor.author Rananga, S
dc.contributor.author Masonta, Moshe T
dc.date.accessioned 2024-04-12T12:34:24Z
dc.date.available 2024-04-12T12:34:24Z
dc.date.issued 2023-08
dc.identifier.citation Otto, A., Rananga, S. & Masonta, M.T. 2023. Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting. http://hdl.handle.net/10204/13663 . en_ZA
dc.identifier.uri http://hdl.handle.net/10204/13663
dc.description.abstract Radio Frequency (RF) Fingerprinting is the theory of identifying a wireless device based on its unique transmitting characteristics which can improve authentication and security in wireless networks. This research project will briefly discuss RF Fingerprinting, Machine Learning, and implement and compare both deep learning and traditional learning techniques for RF Fingerprinting. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://www.satnac.org.za/proceedings en_US
dc.source Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2023, Champagne Sports Resort, 27 - 29 August 2023 en_US
dc.title Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting en_US
dc.type Conference Presentation en_US
dc.description.pages 2 en_US
dc.description.note Paper presented at the Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2023, Champagne Sports Resort, 27 - 29 August 2023 en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea Spectrum Access Mgmt Innov en_US
dc.identifier.apacitation Otto, A., Rananga, S., & Masonta, M. T. (2023). Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting. http://hdl.handle.net/10204/13663 en_ZA
dc.identifier.chicagocitation Otto, AJ, S Rananga, and Moshe T Masonta. "Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting." <i>Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2023, Champagne Sports Resort, 27 - 29 August 2023</i> (2023): http://hdl.handle.net/10204/13663 en_ZA
dc.identifier.vancouvercitation Otto A, Rananga S, Masonta MT, Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting; 2023. http://hdl.handle.net/10204/13663 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Otto, AJ AU - Rananga, S AU - Masonta, Moshe T AB - Radio Frequency (RF) Fingerprinting is the theory of identifying a wireless device based on its unique transmitting characteristics which can improve authentication and security in wireless networks. This research project will briefly discuss RF Fingerprinting, Machine Learning, and implement and compare both deep learning and traditional learning techniques for RF Fingerprinting. DA - 2023-08 DB - ResearchSpace DP - CSIR J1 - Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2023, Champagne Sports Resort, 27 - 29 August 2023 LK - https://researchspace.csir.co.za PY - 2023 T1 - Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting TI - Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting UR - http://hdl.handle.net/10204/13663 ER - en_ZA
dc.identifier.worklist 26982 en_US


Files in this item

Thumbnail
Name: Masonta_2023.pdf
Size: 130.7Kb
Format: PDF
Description: Conference paper
View/Open

This item appears in the following Collection(s)

Show simple item record