Cargando…

Dataset for anomaly detection in a production wireless mesh community network

Wireless community networks, WCN, have proliferated around the world. Cheap off-the-shelf WiFi devices have enabled this new network paradigm where users build their own network infrastructure in a do-it-yourself alternative to traditional network operators. The fact that users are responsible for t...

Descripción completa

Detalles Bibliográficos
Autores principales: Cerdà-Alabern, Llorenç, Iuhasz, Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336394/
https://www.ncbi.nlm.nih.gov/pubmed/37448738
http://dx.doi.org/10.1016/j.dib.2023.109342
_version_ 1785071200584597504
author Cerdà-Alabern, Llorenç
Iuhasz, Gabriel
author_facet Cerdà-Alabern, Llorenç
Iuhasz, Gabriel
author_sort Cerdà-Alabern, Llorenç
collection PubMed
description Wireless community networks, WCN, have proliferated around the world. Cheap off-the-shelf WiFi devices have enabled this new network paradigm where users build their own network infrastructure in a do-it-yourself alternative to traditional network operators. The fact that users are responsible for the administration of their own nodes makes the network very dynamic. There are frequent reboots of the networking devices, and users that join and leave the network. In addition, the unplanned deployment of the network makes it very heterogeneous, with both high and low capacity links. Therefore, anomaly detection in such dynamic scenario is challenging. In this paper we provide a dataset gathered from a production WCN. The data was obtained from a central server that collects data from the mesh nodes that build the network. In total, 63 different nodes were encountered during the data collection. The WCN is used daily to access the Internet from 17 subscribers of the local ISP available on the mesh. We have produced a dataset gathering a large set of features related not only to traffic, but other parameters such as CPU and memory. Furthermore, we provide the network topology of each sample in terms of the adjacency matrix, routing table and routing metrics. In the data we provide there is a known unprovoked gateway failure. Therefore, the dataset can be used to investigate the performance of unsupervised machine learning algorithms for fault detection in WCN. To our knowledge, this is the first dataset that allows fault detection to be investigated from a production WCN.
format Online
Article
Text
id pubmed-10336394
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-103363942023-07-13 Dataset for anomaly detection in a production wireless mesh community network Cerdà-Alabern, Llorenç Iuhasz, Gabriel Data Brief Data Article Wireless community networks, WCN, have proliferated around the world. Cheap off-the-shelf WiFi devices have enabled this new network paradigm where users build their own network infrastructure in a do-it-yourself alternative to traditional network operators. The fact that users are responsible for the administration of their own nodes makes the network very dynamic. There are frequent reboots of the networking devices, and users that join and leave the network. In addition, the unplanned deployment of the network makes it very heterogeneous, with both high and low capacity links. Therefore, anomaly detection in such dynamic scenario is challenging. In this paper we provide a dataset gathered from a production WCN. The data was obtained from a central server that collects data from the mesh nodes that build the network. In total, 63 different nodes were encountered during the data collection. The WCN is used daily to access the Internet from 17 subscribers of the local ISP available on the mesh. We have produced a dataset gathering a large set of features related not only to traffic, but other parameters such as CPU and memory. Furthermore, we provide the network topology of each sample in terms of the adjacency matrix, routing table and routing metrics. In the data we provide there is a known unprovoked gateway failure. Therefore, the dataset can be used to investigate the performance of unsupervised machine learning algorithms for fault detection in WCN. To our knowledge, this is the first dataset that allows fault detection to be investigated from a production WCN. Elsevier 2023-06-25 /pmc/articles/PMC10336394/ /pubmed/37448738 http://dx.doi.org/10.1016/j.dib.2023.109342 Text en © 2023 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
Cerdà-Alabern, Llorenç
Iuhasz, Gabriel
Dataset for anomaly detection in a production wireless mesh community network
title Dataset for anomaly detection in a production wireless mesh community network
title_full Dataset for anomaly detection in a production wireless mesh community network
title_fullStr Dataset for anomaly detection in a production wireless mesh community network
title_full_unstemmed Dataset for anomaly detection in a production wireless mesh community network
title_short Dataset for anomaly detection in a production wireless mesh community network
title_sort dataset for anomaly detection in a production wireless mesh community network
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336394/
https://www.ncbi.nlm.nih.gov/pubmed/37448738
http://dx.doi.org/10.1016/j.dib.2023.109342
work_keys_str_mv AT cerdaalabernllorenc datasetforanomalydetectioninaproductionwirelessmeshcommunitynetwork
AT iuhaszgabriel datasetforanomalydetectioninaproductionwirelessmeshcommunitynetwork