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A Dataset of physical-layer measurements in indoor wireless jamming scenarios

The broadcast nature of wireless communications makes them vulnerable to denial-of-service attacks. Indeed, an adversary can prevent the reception of wireless messages by transmitting signals with high power over the same frequency of the considered channel. This paper presents an experimental datas...

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Detalles Bibliográficos
Autores principales: Alhazbi, Saeif, Sciancalepore, Savio, Oligeri, Gabriele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720428/
https://www.ncbi.nlm.nih.gov/pubmed/36478679
http://dx.doi.org/10.1016/j.dib.2022.108773
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author Alhazbi, Saeif
Sciancalepore, Savio
Oligeri, Gabriele
author_facet Alhazbi, Saeif
Sciancalepore, Savio
Oligeri, Gabriele
author_sort Alhazbi, Saeif
collection PubMed
description The broadcast nature of wireless communications makes them vulnerable to denial-of-service attacks. Indeed, an adversary can prevent the reception of wireless messages by transmitting signals with high power over the same frequency of the considered channel. This paper presents an experimental dataset of real-world indoor communication scenarios affected by different jamming techniques. Specifically, our dataset includes data acquired from 7 different Software Defined Radios (SDRs), i.e., the USRP Ettus Research X310, operating in an office environment. Each experiment is characterized by a transmitter, a receiver, and a jammer. While the hardware of the transmitter and the receiver are kept the same for all the experiments, the hardware of the jammer is changed adopting 5 different radios of the same brand. The dataset includes different jamming behaviors, based on the type of signal injected by the jammer: no jamming (silent), tone (sinusoidal), and Gaussian noise. Moreover, besides having multiple jamming devices and modes, the dataset also includes different transmission distances and jamming powers. In each experiment, a pre-determined sequence of bits has been modulated using the BPSK scheme, transmitted wirelessly under different jamming conditions, and then stored, at the receiver, as a 2-columns matrix of I/Q samples. Researchers can use this dataset in several ways, including: (i) developing active and reactive techniques for jamming detection, (ii) jamming identification at the physical layer, and finally, (iii) developing mitigation techniques supported by real data.
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spelling pubmed-97204282022-12-06 A Dataset of physical-layer measurements in indoor wireless jamming scenarios Alhazbi, Saeif Sciancalepore, Savio Oligeri, Gabriele Data Brief Data Article The broadcast nature of wireless communications makes them vulnerable to denial-of-service attacks. Indeed, an adversary can prevent the reception of wireless messages by transmitting signals with high power over the same frequency of the considered channel. This paper presents an experimental dataset of real-world indoor communication scenarios affected by different jamming techniques. Specifically, our dataset includes data acquired from 7 different Software Defined Radios (SDRs), i.e., the USRP Ettus Research X310, operating in an office environment. Each experiment is characterized by a transmitter, a receiver, and a jammer. While the hardware of the transmitter and the receiver are kept the same for all the experiments, the hardware of the jammer is changed adopting 5 different radios of the same brand. The dataset includes different jamming behaviors, based on the type of signal injected by the jammer: no jamming (silent), tone (sinusoidal), and Gaussian noise. Moreover, besides having multiple jamming devices and modes, the dataset also includes different transmission distances and jamming powers. In each experiment, a pre-determined sequence of bits has been modulated using the BPSK scheme, transmitted wirelessly under different jamming conditions, and then stored, at the receiver, as a 2-columns matrix of I/Q samples. Researchers can use this dataset in several ways, including: (i) developing active and reactive techniques for jamming detection, (ii) jamming identification at the physical layer, and finally, (iii) developing mitigation techniques supported by real data. Elsevier 2022-11-23 /pmc/articles/PMC9720428/ /pubmed/36478679 http://dx.doi.org/10.1016/j.dib.2022.108773 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Alhazbi, Saeif
Sciancalepore, Savio
Oligeri, Gabriele
A Dataset of physical-layer measurements in indoor wireless jamming scenarios
title A Dataset of physical-layer measurements in indoor wireless jamming scenarios
title_full A Dataset of physical-layer measurements in indoor wireless jamming scenarios
title_fullStr A Dataset of physical-layer measurements in indoor wireless jamming scenarios
title_full_unstemmed A Dataset of physical-layer measurements in indoor wireless jamming scenarios
title_short A Dataset of physical-layer measurements in indoor wireless jamming scenarios
title_sort dataset of physical-layer measurements in indoor wireless jamming scenarios
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720428/
https://www.ncbi.nlm.nih.gov/pubmed/36478679
http://dx.doi.org/10.1016/j.dib.2022.108773
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