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Learning Mixed Traffic Signatures in Shared Networks
On shared, wide-area networks (WAN), it can be difficult to characterise the current traffic. There can be different protocols in use, by multiple data streams, producing a mix of different traffic signatures. Furthermore, bottlenecks and protocols can change dynamically. Yet, if it were possible to...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302268/ http://dx.doi.org/10.1007/978-3-030-50371-0_39 |
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author | Anvari, Hamidreza Lu, Paul |
author_facet | Anvari, Hamidreza Lu, Paul |
author_sort | Anvari, Hamidreza |
collection | PubMed |
description | On shared, wide-area networks (WAN), it can be difficult to characterise the current traffic. There can be different protocols in use, by multiple data streams, producing a mix of different traffic signatures. Furthermore, bottlenecks and protocols can change dynamically. Yet, if it were possible to determine the protocols (e.g., congestion control algorithms (CCAs)) or the applications in use by the background traffic, appropriate optimisations for the foreground traffic might be taken by operating systems, users, or administrators. We extend previous work in predicting network protocols via signatures based on a time-series of round-trip times (RTT). Gathering RTTs is minimally intrusive and does not require administrative privilege. Although there have been successes in using machine learning (ML) to classify protocols, the use cases have been relatively simple or have focused on the foreground traffic. We show that both k-nearest-neighbour (K-NN) with dynamic time warp (DTW), and multi-layer perceptrons (MLP), can classify (with useful accuracy) background traffic signatures with a range of bottleneck bandwidths. |
format | Online Article Text |
id | pubmed-7302268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73022682020-06-18 Learning Mixed Traffic Signatures in Shared Networks Anvari, Hamidreza Lu, Paul Computational Science – ICCS 2020 Article On shared, wide-area networks (WAN), it can be difficult to characterise the current traffic. There can be different protocols in use, by multiple data streams, producing a mix of different traffic signatures. Furthermore, bottlenecks and protocols can change dynamically. Yet, if it were possible to determine the protocols (e.g., congestion control algorithms (CCAs)) or the applications in use by the background traffic, appropriate optimisations for the foreground traffic might be taken by operating systems, users, or administrators. We extend previous work in predicting network protocols via signatures based on a time-series of round-trip times (RTT). Gathering RTTs is minimally intrusive and does not require administrative privilege. Although there have been successes in using machine learning (ML) to classify protocols, the use cases have been relatively simple or have focused on the foreground traffic. We show that both k-nearest-neighbour (K-NN) with dynamic time warp (DTW), and multi-layer perceptrons (MLP), can classify (with useful accuracy) background traffic signatures with a range of bottleneck bandwidths. 2020-05-26 /pmc/articles/PMC7302268/ http://dx.doi.org/10.1007/978-3-030-50371-0_39 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Anvari, Hamidreza Lu, Paul Learning Mixed Traffic Signatures in Shared Networks |
title | Learning Mixed Traffic Signatures in Shared Networks |
title_full | Learning Mixed Traffic Signatures in Shared Networks |
title_fullStr | Learning Mixed Traffic Signatures in Shared Networks |
title_full_unstemmed | Learning Mixed Traffic Signatures in Shared Networks |
title_short | Learning Mixed Traffic Signatures in Shared Networks |
title_sort | learning mixed traffic signatures in shared networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302268/ http://dx.doi.org/10.1007/978-3-030-50371-0_39 |
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