Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/8868
Title: Detección Robusta por Grupos de Señales Primarias
Author: Jimenez Blasco, Mercedes  
Rifà-Pous, Helena  
Mut Rojas, Jose Carlos
Citation: Jimenez, M.; Mut, J. C.; Rifà, H.. (2010). "Detección robusta por grupos de señales primarias en redes de radio cognitiva". A:Solanas, A.; Martínez, A.; Castellà, J.; Domingo, J. Actas de la XI Reunión Española sobre Criptología y Seguridad de la Información .TARRAGONA.Publicacions URV. Pág. 371 - 376. ISBN: 978-84-693-3304-4.
Abstract: The cognitive radio is a wireless technology proposed to use efficiently the resources of radio spectrum allowing to reduce the burden existing on the frequency free use bands. Cognitive radio networks are able to scan the spectrum and adapt their parameters to operate in the unoccupied bands. To avoid interfering with licensed users operating in a particular channel, the sensitivity of the networks have to be very high. This is achieved by cooperative detection methods. The current cooperative detection methods have a lack of robustness against attacks either occasional or continuous. We present a method of fusion groups in mind the behavior of users in the short and long term. When to merge the data, the method is based on giving greater weight to groups of users with greater unanimity in their decisions. The simulation results show that in the presence of attacker, the fusion method proposed by groups achieved a detection superior to other methods, meeting the minimum sensitivity requirements of cognitive radio networks even with 12 users or malicious repeatedly, 10 specific attackers.
Keywords: detection
security
cognitive radio networks
Document type: info:eu-repo/semantics/article
Issue Date: 7-Sep-2010
Publication license: http://creativecommons.org/licenses/by-nc-nd/3.0/es/  
Appears in Collections:Articles cientÍfics
Conferències

Files in This Item:
File Description SizeFormat 
JimenezBlasco_RECSI2010_DeteccionRobusta.pdf180,21 kBAdobe PDFThumbnail
View/Open