An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking

Network traffic classification is basic tool for internet service providers, various government and private organisations to carry out investigation on network activities such as Intrusion Detection Systems (IDS), security monitoring, lawful interception and Quality of Service (QoS). Recent network...

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Autores principales:	Abdulmohsen Almalawi, Adil Fahad
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Internet traffic classification semi-supervised learning multiview Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5254fb8113e145e7ba37f8d22cfb04b3
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id	oai:doaj.org-article:5254fb8113e145e7ba37f8d22cfb04b3
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spelling	oai:doaj.org-article:5254fb8113e145e7ba37f8d22cfb04b32021-11-17T00:01:13ZAn Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking2169-353610.1109/ACCESS.2021.3123451https://doaj.org/article/5254fb8113e145e7ba37f8d22cfb04b32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591648/https://doaj.org/toc/2169-3536Network traffic classification is basic tool for internet service providers, various government and private organisations to carry out investigation on network activities such as Intrusion Detection Systems (IDS), security monitoring, lawful interception and Quality of Service (QoS). Recent network traffic classification approaches have used an extracted and predefined class label which come from multiple experts to build a robust network traffic classifier. However, keeping IP traffic classifiers up to date requires large amounts of new emerging labeled traffic flows which is often expensive and time-consuming. This paper proposes an efficient network classification (named Net-Stack) which inherits the advantages of various widths clustering and semi-supervised stacking to minimize the shortage of labeled flows, and accurately learn IP traffic features and knowledge. The Net-Stack approach consists of four stages. The first stage pre-processes the traffic data and removes noise traffic observations based on various widths clustering to select most representative observations from both the local and global perspective. The second stage generates strong discrimination ability for multiview representations of the original data using dimensionality reduction techniques. The third stage involves heterogeneous semi-supervised learning algorithms to exploit the complementary information contained in multiple views to refine the decision boundaries for each traffic class and get a low dimensional metadata representation. The final stage employs a meta-classifier and stacking approach to comprehensively learn from the metadata representation obtained in stage three for improving the generalization performance and predicting final classification decision. Experimental study on twelve traffic data sets shows the effectiveness of our proposed Net-Stack approach compared to the baseline methods when there is relatively less labelled training data available.Abdulmohsen AlmalawiAdil FahadIEEEarticleInternet traffic classificationsemi-supervised learningmultiviewElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151681-151696 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Internet traffic classification semi-supervised learning multiview Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Internet traffic classification semi-supervised learning multiview Electrical engineering. Electronics. Nuclear engineering TK1-9971 Abdulmohsen Almalawi Adil Fahad An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking
description	Network traffic classification is basic tool for internet service providers, various government and private organisations to carry out investigation on network activities such as Intrusion Detection Systems (IDS), security monitoring, lawful interception and Quality of Service (QoS). Recent network traffic classification approaches have used an extracted and predefined class label which come from multiple experts to build a robust network traffic classifier. However, keeping IP traffic classifiers up to date requires large amounts of new emerging labeled traffic flows which is often expensive and time-consuming. This paper proposes an efficient network classification (named Net-Stack) which inherits the advantages of various widths clustering and semi-supervised stacking to minimize the shortage of labeled flows, and accurately learn IP traffic features and knowledge. The Net-Stack approach consists of four stages. The first stage pre-processes the traffic data and removes noise traffic observations based on various widths clustering to select most representative observations from both the local and global perspective. The second stage generates strong discrimination ability for multiview representations of the original data using dimensionality reduction techniques. The third stage involves heterogeneous semi-supervised learning algorithms to exploit the complementary information contained in multiple views to refine the decision boundaries for each traffic class and get a low dimensional metadata representation. The final stage employs a meta-classifier and stacking approach to comprehensively learn from the metadata representation obtained in stage three for improving the generalization performance and predicting final classification decision. Experimental study on twelve traffic data sets shows the effectiveness of our proposed Net-Stack approach compared to the baseline methods when there is relatively less labelled training data available.
format	article
author	Abdulmohsen Almalawi Adil Fahad
author_facet	Abdulmohsen Almalawi Adil Fahad
author_sort	Abdulmohsen Almalawi
title	An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking
title_short	An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking
title_full	An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking
title_fullStr	An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking
title_full_unstemmed	An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking
title_sort	efficient network classification based on various-widths clustering and semi-supervised stacking
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5254fb8113e145e7ba37f8d22cfb04b3
work_keys_str_mv	AT abdulmohsenalmalawi anefficientnetworkclassificationbasedonvariouswidthsclusteringandsemisupervisedstacking AT adilfahad anefficientnetworkclassificationbasedonvariouswidthsclusteringandsemisupervisedstacking AT abdulmohsenalmalawi efficientnetworkclassificationbasedonvariouswidthsclusteringandsemisupervisedstacking AT adilfahad efficientnetworkclassificationbasedonvariouswidthsclusteringandsemisupervisedstacking
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An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking

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