A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning

This research presents a novel combined learning method for developing a novel DDoS model that is expandable and flexible property of deep learning. This method can advance the current practice and problems in DDoS detection. A combined method of deep learning with knowledge-graph classification is...

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Autor principal:	Tekleselassie Hailye
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	distributed denial of service wireless networks deep learning algorithms transmission control protocol cnn network security Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/c5e4c6dcc6174c84b1f82c1ec536b1c6
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id	oai:doaj.org-article:c5e4c6dcc6174c84b1f82c1ec536b1c6
record_format	dspace
spelling	oai:doaj.org-article:c5e4c6dcc6174c84b1f82c1ec536b1c62021-12-02T17:13:38ZA Deep Learning Approach for DDoS Attack Detection Using Supervised Learning2261-236X10.1051/matecconf/202134801012https://doaj.org/article/c5e4c6dcc6174c84b1f82c1ec536b1c62021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/17/matecconf_inbes2021_01012.pdfhttps://doaj.org/toc/2261-236XThis research presents a novel combined learning method for developing a novel DDoS model that is expandable and flexible property of deep learning. This method can advance the current practice and problems in DDoS detection. A combined method of deep learning with knowledge-graph classification is proposed for DDoS detection. Whereas deep learning algorithm is used to develop a classifier model, knowledge-graph system makes the model expandable and flexible. It is analytically verified with CICIDS2017 dataset of 53.127 entire occurrences, by using ten-fold cross validation. Experimental outcome indicates that 99.97% performance is registered after connection. Fascinatingly, significant knowledge ironic learning for DDoS detection varies as a basic behavior of DDoS detection and prevention methods. So, security professionals are suggested to mix DDoS detection in their internet and network.Tekleselassie HailyeEDP Sciencesarticledistributed denial of servicewireless networksdeep learning algorithmstransmission control protocolcnnnetwork securityEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 348, p 01012 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	distributed denial of service wireless networks deep learning algorithms transmission control protocol cnn network security Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	distributed denial of service wireless networks deep learning algorithms transmission control protocol cnn network security Engineering (General). Civil engineering (General) TA1-2040 Tekleselassie Hailye A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning
description	This research presents a novel combined learning method for developing a novel DDoS model that is expandable and flexible property of deep learning. This method can advance the current practice and problems in DDoS detection. A combined method of deep learning with knowledge-graph classification is proposed for DDoS detection. Whereas deep learning algorithm is used to develop a classifier model, knowledge-graph system makes the model expandable and flexible. It is analytically verified with CICIDS2017 dataset of 53.127 entire occurrences, by using ten-fold cross validation. Experimental outcome indicates that 99.97% performance is registered after connection. Fascinatingly, significant knowledge ironic learning for DDoS detection varies as a basic behavior of DDoS detection and prevention methods. So, security professionals are suggested to mix DDoS detection in their internet and network.
format	article
author	Tekleselassie Hailye
author_facet	Tekleselassie Hailye
author_sort	Tekleselassie Hailye
title	A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning
title_short	A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning
title_full	A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning
title_fullStr	A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning
title_full_unstemmed	A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning
title_sort	deep learning approach for ddos attack detection using supervised learning
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/c5e4c6dcc6174c84b1f82c1ec536b1c6
work_keys_str_mv	AT tekleselassiehailye adeeplearningapproachforddosattackdetectionusingsupervisedlearning AT tekleselassiehailye deeplearningapproachforddosattackdetectionusingsupervisedlearning
_version_	1718381345032372224

A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning

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