A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree

Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) pla...

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Autores principales:	B. Ida Seraphim, E. Poovammal, Kadiyala Ramana, Natalia Kryvinska, N. Penchalaiah
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	intrusion detection system (ids) stream data mining stacked autoencoder dpso hoeffding tree feature selection Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a51fb53b058d4f278936916ceb3a8b6e
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spelling	oai:doaj.org-article:a51fb53b058d4f278936916ceb3a8b6e2021-11-23T03:03:38ZA hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree10.3934/mbe.20213981551-0018https://doaj.org/article/a51fb53b058d4f278936916ceb3a8b6e2021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021398?viewType=HTMLhttps://doaj.org/toc/1551-0018Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) plays a crucial role in identifying the data and user deviations in an organization. In this paper, stream data mining is incorporated with an IDS to do a specific task. The task is to distinguish the important, covered up information successfully in less amount of time. The experiment focuses on improving the effectiveness of an IDS using the proposed Stacked Autoencoder Hoeffding Tree approach (SAE-HT) using Darwinian Particle Swarm Optimization (DPSO) for feature selection. The experiment is performed in NSL_KDD dataset the important features are obtained using DPSO and the classification is performed using proposed SAE-HT technique. The proposed technique achieves a higher accuracy of 97.7% when compared with all the other state-of-art techniques. It is observed that the proposed technique increases the accuracy and detection rate thus reducing the false alarm rate.B. Ida SeraphimE. PoovammalKadiyala Ramana Natalia KryvinskaN. PenchalaiahAIMS Pressarticleintrusion detection system (ids)stream data miningstacked autoencoderdpsohoeffding treefeature selectionBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8024-8044 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intrusion detection system (ids) stream data mining stacked autoencoder dpso hoeffding tree feature selection Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	intrusion detection system (ids) stream data mining stacked autoencoder dpso hoeffding tree feature selection Biotechnology TP248.13-248.65 Mathematics QA1-939 B. Ida Seraphim E. Poovammal Kadiyala Ramana Natalia Kryvinska N. Penchalaiah A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree
description	Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) plays a crucial role in identifying the data and user deviations in an organization. In this paper, stream data mining is incorporated with an IDS to do a specific task. The task is to distinguish the important, covered up information successfully in less amount of time. The experiment focuses on improving the effectiveness of an IDS using the proposed Stacked Autoencoder Hoeffding Tree approach (SAE-HT) using Darwinian Particle Swarm Optimization (DPSO) for feature selection. The experiment is performed in NSL_KDD dataset the important features are obtained using DPSO and the classification is performed using proposed SAE-HT technique. The proposed technique achieves a higher accuracy of 97.7% when compared with all the other state-of-art techniques. It is observed that the proposed technique increases the accuracy and detection rate thus reducing the false alarm rate.
format	article
author	B. Ida Seraphim E. Poovammal Kadiyala Ramana Natalia Kryvinska N. Penchalaiah
author_facet	B. Ida Seraphim E. Poovammal Kadiyala Ramana Natalia Kryvinska N. Penchalaiah
author_sort	B. Ida Seraphim
title	A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree
title_short	A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree
title_full	A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree
title_fullStr	A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree
title_full_unstemmed	A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree
title_sort	hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/a51fb53b058d4f278936916ceb3a8b6e
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A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree

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