TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification

Distributed Denial of Service (DDoS) attack is one of the most dangerous attacks that result in bringing down the server(s) and it is essential to classify the exact attack to implement robust security measures. In this work, we present an approach for detecting the prominent DDoS attacks that can b...

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Autores principales:	Onkar Thorat, Nirali Parekh, Ramchandra Mangrulkar
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Information management security Machine learning Distributed denial of service Reflection attack Exploitation attack Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/9ce2e788300f4c1bb5a0a36f0605e672
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id	oai:doaj.org-article:9ce2e788300f4c1bb5a0a36f0605e672
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spelling	oai:doaj.org-article:9ce2e788300f4c1bb5a0a36f0605e6722021-11-26T04:42:46ZTaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification2667-096810.1016/j.jjimei.2021.100048https://doaj.org/article/9ce2e788300f4c1bb5a0a36f0605e6722021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2667096821000410https://doaj.org/toc/2667-0968Distributed Denial of Service (DDoS) attack is one of the most dangerous attacks that result in bringing down the server(s) and it is essential to classify the exact attack to implement robust security measures. In this work, we present an approach for detecting the prominent DDoS attacks that can be carried over Transport Layer protocols. Four different levels are taken into consideration which helps to classify one of the 11 different attacks. A bigger problem is divided into smaller ones and then conquered. This approach, called TaxoDaCML - Taxonomy-based Divide and Conquer approach using ML minimizes computational cost and at the same time maintains the required accuracy. Results prove that our approach achieves 99.9% accuracy for DDoS attack detection and more than 85% for DDoS attack classification. Comparison of TaxoDaCML is done with the previous works and is found to perform better for DDoS attacks classification.Onkar ThoratNirali ParekhRamchandra MangrulkarElsevierarticleInformation management securityMachine learningDistributed denial of serviceReflection attackExploitation attackInformation technologyT58.5-58.64ENInternational Journal of Information Management Data Insights, Vol 1, Iss 2, Pp 100048- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Information management security Machine learning Distributed denial of service Reflection attack Exploitation attack Information technology T58.5-58.64
spellingShingle	Information management security Machine learning Distributed denial of service Reflection attack Exploitation attack Information technology T58.5-58.64 Onkar Thorat Nirali Parekh Ramchandra Mangrulkar TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
description	Distributed Denial of Service (DDoS) attack is one of the most dangerous attacks that result in bringing down the server(s) and it is essential to classify the exact attack to implement robust security measures. In this work, we present an approach for detecting the prominent DDoS attacks that can be carried over Transport Layer protocols. Four different levels are taken into consideration which helps to classify one of the 11 different attacks. A bigger problem is divided into smaller ones and then conquered. This approach, called TaxoDaCML - Taxonomy-based Divide and Conquer approach using ML minimizes computational cost and at the same time maintains the required accuracy. Results prove that our approach achieves 99.9% accuracy for DDoS attack detection and more than 85% for DDoS attack classification. Comparison of TaxoDaCML is done with the previous works and is found to perform better for DDoS attacks classification.
format	article
author	Onkar Thorat Nirali Parekh Ramchandra Mangrulkar
author_facet	Onkar Thorat Nirali Parekh Ramchandra Mangrulkar
author_sort	Onkar Thorat
title	TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
title_short	TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
title_full	TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
title_fullStr	TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
title_full_unstemmed	TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
title_sort	taxodacml: taxonomy based divide and conquer using machine learning approach for ddos attack classification
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/9ce2e788300f4c1bb5a0a36f0605e672
work_keys_str_mv	AT onkarthorat taxodacmltaxonomybaseddivideandconquerusingmachinelearningapproachforddosattackclassification AT niraliparekh taxodacmltaxonomybaseddivideandconquerusingmachinelearningapproachforddosattackclassification AT ramchandramangrulkar taxodacmltaxonomybaseddivideandconquerusingmachinelearningapproachforddosattackclassification
_version_	1718409796874403840

TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification

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