Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

Currently, with the increasing number of devices connected to the Internet, search for network vulnerabilities to attackers has increased, and protection systems have become indispensable. There are prevalent security attacks, such as the Distributed Denial of Service (DDoS), which have been causing...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Robson V. Mendonca, Arthur A. M. Teodoro, Renata L. Rosa, Muhammad Saadi, Dick Carrillo Melgarejo, Pedro H. J. Nardelli, Demostenes Z. Rodriguez
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Activation function deep learning intrusion detection system Tree-CNN Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/27435b6fa15045c1a516c66d26e484e3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:27435b6fa15045c1a516c66d26e484e3
record_format	dspace
spelling	oai:doaj.org-article:27435b6fa15045c1a516c66d26e484e32021-11-19T00:05:29ZIntrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network2169-353610.1109/ACCESS.2021.3074664https://doaj.org/article/27435b6fa15045c1a516c66d26e484e32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9410227/https://doaj.org/toc/2169-3536Currently, with the increasing number of devices connected to the Internet, search for network vulnerabilities to attackers has increased, and protection systems have become indispensable. There are prevalent security attacks, such as the Distributed Denial of Service (DDoS), which have been causing significant damage to companies. However, through security attacks, it is possible to extract characteristics that identify the type of attack. Thus, it is essential to have fast and effective security identification models. In this work, a novel Intrusion Detection System (IDS) based on the Tree-CNN hierarchical algorithm with the Soft-Root-Sign (SRS) activation function is proposed. The model reduces the training time of the generated model for detecting DDoS, Infiltration, Brute Force, and Web attacks. For performance assessment, the model is implemented in a medium-sized company, analyzing the level of complexity of the proposed solution. Experimental results demonstrate that the proposed hierarchical model achieves a significant reduction in execution time, around 36%, and an average detection accuracy of 0.98 considering all the analyzed attacks. Therefore, the results of performance evaluation show that the proposed classifier based on Tree-CNN is of low complexity and requires less processing time and computational resources, outperforming other current IDS based on machine learning algorithms.Robson V. MendoncaArthur A. M. TeodoroRenata L. RosaMuhammad SaadiDick Carrillo MelgarejoPedro H. J. NardelliDemostenes Z. RodriguezIEEEarticleActivation functiondeep learningintrusion detection systemTree-CNNElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 61024-61034 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Activation function deep learning intrusion detection system Tree-CNN Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Activation function deep learning intrusion detection system Tree-CNN Electrical engineering. Electronics. Nuclear engineering TK1-9971 Robson V. Mendonca Arthur A. M. Teodoro Renata L. Rosa Muhammad Saadi Dick Carrillo Melgarejo Pedro H. J. Nardelli Demostenes Z. Rodriguez Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
description	Currently, with the increasing number of devices connected to the Internet, search for network vulnerabilities to attackers has increased, and protection systems have become indispensable. There are prevalent security attacks, such as the Distributed Denial of Service (DDoS), which have been causing significant damage to companies. However, through security attacks, it is possible to extract characteristics that identify the type of attack. Thus, it is essential to have fast and effective security identification models. In this work, a novel Intrusion Detection System (IDS) based on the Tree-CNN hierarchical algorithm with the Soft-Root-Sign (SRS) activation function is proposed. The model reduces the training time of the generated model for detecting DDoS, Infiltration, Brute Force, and Web attacks. For performance assessment, the model is implemented in a medium-sized company, analyzing the level of complexity of the proposed solution. Experimental results demonstrate that the proposed hierarchical model achieves a significant reduction in execution time, around 36%, and an average detection accuracy of 0.98 considering all the analyzed attacks. Therefore, the results of performance evaluation show that the proposed classifier based on Tree-CNN is of low complexity and requires less processing time and computational resources, outperforming other current IDS based on machine learning algorithms.
format	article
author	Robson V. Mendonca Arthur A. M. Teodoro Renata L. Rosa Muhammad Saadi Dick Carrillo Melgarejo Pedro H. J. Nardelli Demostenes Z. Rodriguez
author_facet	Robson V. Mendonca Arthur A. M. Teodoro Renata L. Rosa Muhammad Saadi Dick Carrillo Melgarejo Pedro H. J. Nardelli Demostenes Z. Rodriguez
author_sort	Robson V. Mendonca
title	Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
title_short	Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
title_full	Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
title_fullStr	Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
title_full_unstemmed	Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
title_sort	intrusion detection system based on fast hierarchical deep convolutional neural network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/27435b6fa15045c1a516c66d26e484e3
work_keys_str_mv	AT robsonvmendonca intrusiondetectionsystembasedonfasthierarchicaldeepconvolutionalneuralnetwork AT arthuramteodoro intrusiondetectionsystembasedonfasthierarchicaldeepconvolutionalneuralnetwork AT renatalrosa intrusiondetectionsystembasedonfasthierarchicaldeepconvolutionalneuralnetwork AT muhammadsaadi intrusiondetectionsystembasedonfasthierarchicaldeepconvolutionalneuralnetwork AT dickcarrillomelgarejo intrusiondetectionsystembasedonfasthierarchicaldeepconvolutionalneuralnetwork AT pedrohjnardelli intrusiondetectionsystembasedonfasthierarchicaldeepconvolutionalneuralnetwork AT demosteneszrodriguez intrusiondetectionsystembasedonfasthierarchicaldeepconvolutionalneuralnetwork
_version_	1718420657801265152

Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

Ejemplares similares