Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks

The great commitment in different areas of computer science for the study of computer networks used to fulfill specific and major business tasks has generated a need for their maintenance and optimal operability. Distributed denial of service (DDoS) is a frequent threat to computer networks because...

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Autores principales:	Andrés Chartuni, José Márquez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	computer networks data preprocessing DDoS attack machine learning neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a4536efd87e14040a926cbb3be9b2856
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spelling	oai:doaj.org-article:a4536efd87e14040a926cbb3be9b28562021-11-25T16:32:52ZMulti-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks10.3390/app1122106092076-3417https://doaj.org/article/a4536efd87e14040a926cbb3be9b28562021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10609https://doaj.org/toc/2076-3417The great commitment in different areas of computer science for the study of computer networks used to fulfill specific and major business tasks has generated a need for their maintenance and optimal operability. Distributed denial of service (DDoS) is a frequent threat to computer networks because of its disruption to the services they cause. This disruption results in the instability and/or inoperability of the network. There are different classes of DDoS attacks, each with a different mode of operation, so detecting them has become a difficult task for network monitoring and control systems. The objective of this work is based on the exploration and choice of a set of data that represents DDoS attack events, on their treatment in a preprocessing phase, and later, the generation of a model of sequential neural networks of multi-class classification. This is done to identify and classify the various types of DDoS attacks. The result was compared with previous works treating the same dataset used herein. We compared their classification method, against ours. During this research, the CIC DDoS2019 dataset was used. Previous works carried out with this dataset proposed a binary classification approach, our approach is based on multi-classification. Our proposed model was capable of achieving around 94% in metrics such as precision, accuracy, recall and F1 score. The added value of multiclass classification during this work is identified and compared with binary classifications using the models presented in the previous.Andrés ChartuniJosé MárquezMDPI AGarticlecomputer networksdata preprocessingDDoS attackmachine learningneural networksTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10609, p 10609 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	computer networks data preprocessing DDoS attack machine learning neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	computer networks data preprocessing DDoS attack machine learning neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Andrés Chartuni José Márquez Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks
description	The great commitment in different areas of computer science for the study of computer networks used to fulfill specific and major business tasks has generated a need for their maintenance and optimal operability. Distributed denial of service (DDoS) is a frequent threat to computer networks because of its disruption to the services they cause. This disruption results in the instability and/or inoperability of the network. There are different classes of DDoS attacks, each with a different mode of operation, so detecting them has become a difficult task for network monitoring and control systems. The objective of this work is based on the exploration and choice of a set of data that represents DDoS attack events, on their treatment in a preprocessing phase, and later, the generation of a model of sequential neural networks of multi-class classification. This is done to identify and classify the various types of DDoS attacks. The result was compared with previous works treating the same dataset used herein. We compared their classification method, against ours. During this research, the CIC DDoS2019 dataset was used. Previous works carried out with this dataset proposed a binary classification approach, our approach is based on multi-classification. Our proposed model was capable of achieving around 94% in metrics such as precision, accuracy, recall and F1 score. The added value of multiclass classification during this work is identified and compared with binary classifications using the models presented in the previous.
format	article
author	Andrés Chartuni José Márquez
author_facet	Andrés Chartuni José Márquez
author_sort	Andrés Chartuni
title	Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks
title_short	Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks
title_full	Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks
title_fullStr	Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks
title_full_unstemmed	Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks
title_sort	multi-classifier of ddos attacks in computer networks built on neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a4536efd87e14040a926cbb3be9b2856
work_keys_str_mv	AT andreschartuni multiclassifierofddosattacksincomputernetworksbuiltonneuralnetworks AT josemarquez multiclassifierofddosattacksincomputernetworksbuiltonneuralnetworks
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Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks

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