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Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks

Cooperative spectrum sensing (CSS) is envisaged as a powerful approach to improve the utilization of scarce radio spectrum resources, but it is threatened by Byzantine attack. Byzantine attack has been becoming a popular research topic in both academia and industry due to the demanding requirements...

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Detalles Bibliográficos
Autores principales: Wu, Jun, Song, Tiecheng, Yu, Yue, Wang, Cong, Hu, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033420/
https://www.ncbi.nlm.nih.gov/pubmed/29975727
http://dx.doi.org/10.1371/journal.pone.0199546
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author Wu, Jun
Song, Tiecheng
Yu, Yue
Wang, Cong
Hu, Jing
author_facet Wu, Jun
Song, Tiecheng
Yu, Yue
Wang, Cong
Hu, Jing
author_sort Wu, Jun
collection PubMed
description Cooperative spectrum sensing (CSS) is envisaged as a powerful approach to improve the utilization of scarce radio spectrum resources, but it is threatened by Byzantine attack. Byzantine attack has been becoming a popular research topic in both academia and industry due to the demanding requirements of security. Extensive research mainly aims at mitigating the negative effect of Byzantine attack on CSS, but with some strong assumptions, such as attackers are in minority or trusted node(s) exist for data fusion, while paying little attention to a mobile scenario. This paper focuses on the issue of designing a general and reliable reference for CSS in a mobile network. Instead of the previously simplified attack, we develop a generic Byzantine attack model from sophisticated behaviors to conduct various attack strategies and derive the condition of which Byzantine attack makes the fusion center (FC) blind. Specifically, we propose a robust sequential CSS (SCSS) against dynamic Byzantine attack. Our proposed method solves the unreliability of the FC by means of delivery-based assessment to check consistency of individual sensing report, and innovatively reuses the sensing information from Byzantines via a novel weight allocation mechanism. Furthermore, trust value (TrV) ranking is exploited to proceed with a sequential test which generates a more accurate decision about the presence of phenomenon with fewer samples. Lastly, we carry out simulations on comparison of existing data fusion technologies and SCSS under dynamic Byzantine attack, and results verify the theoretical analysis and effectiveness of our proposed approach. We also conduct numerical analyses to demonstrate explicit impacts of secondary user (SU) density and mobility on the performance of SCSS.
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spelling pubmed-60334202018-07-19 Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks Wu, Jun Song, Tiecheng Yu, Yue Wang, Cong Hu, Jing PLoS One Research Article Cooperative spectrum sensing (CSS) is envisaged as a powerful approach to improve the utilization of scarce radio spectrum resources, but it is threatened by Byzantine attack. Byzantine attack has been becoming a popular research topic in both academia and industry due to the demanding requirements of security. Extensive research mainly aims at mitigating the negative effect of Byzantine attack on CSS, but with some strong assumptions, such as attackers are in minority or trusted node(s) exist for data fusion, while paying little attention to a mobile scenario. This paper focuses on the issue of designing a general and reliable reference for CSS in a mobile network. Instead of the previously simplified attack, we develop a generic Byzantine attack model from sophisticated behaviors to conduct various attack strategies and derive the condition of which Byzantine attack makes the fusion center (FC) blind. Specifically, we propose a robust sequential CSS (SCSS) against dynamic Byzantine attack. Our proposed method solves the unreliability of the FC by means of delivery-based assessment to check consistency of individual sensing report, and innovatively reuses the sensing information from Byzantines via a novel weight allocation mechanism. Furthermore, trust value (TrV) ranking is exploited to proceed with a sequential test which generates a more accurate decision about the presence of phenomenon with fewer samples. Lastly, we carry out simulations on comparison of existing data fusion technologies and SCSS under dynamic Byzantine attack, and results verify the theoretical analysis and effectiveness of our proposed approach. We also conduct numerical analyses to demonstrate explicit impacts of secondary user (SU) density and mobility on the performance of SCSS. Public Library of Science 2018-07-05 /pmc/articles/PMC6033420/ /pubmed/29975727 http://dx.doi.org/10.1371/journal.pone.0199546 Text en © 2018 Wu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wu, Jun
Song, Tiecheng
Yu, Yue
Wang, Cong
Hu, Jing
Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks
title Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks
title_full Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks
title_fullStr Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks
title_full_unstemmed Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks
title_short Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks
title_sort sequential cooperative spectrum sensing in the presence of dynamic byzantine attack for mobile networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033420/
https://www.ncbi.nlm.nih.gov/pubmed/29975727
http://dx.doi.org/10.1371/journal.pone.0199546
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