Intrusion detection for network based cloud computing by custom RC-NN and optimization

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is give...

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Autores principales:	T. Thilagam, R. Aruna
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Intrusion detection Neural networks Deep learning Cloud computing Network security Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/48605cf8490b455d837408d0b21e3dda
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spelling	oai:doaj.org-article:48605cf8490b455d837408d0b21e3dda2021-11-30T04:16:42ZIntrusion detection for network based cloud computing by custom RC-NN and optimization2405-959510.1016/j.icte.2021.04.006https://doaj.org/article/48605cf8490b455d837408d0b21e3dda2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000503https://doaj.org/toc/2405-9595Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.T. ThilagamR. ArunaElsevierarticleIntrusion detectionNeural networksDeep learningCloud computingNetwork securityInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 512-520 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Intrusion detection Neural networks Deep learning Cloud computing Network security Information technology T58.5-58.64
spellingShingle	Intrusion detection Neural networks Deep learning Cloud computing Network security Information technology T58.5-58.64 T. Thilagam R. Aruna Intrusion detection for network based cloud computing by custom RC-NN and optimization
description	Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.
format	article
author	T. Thilagam R. Aruna
author_facet	T. Thilagam R. Aruna
author_sort	T. Thilagam
title	Intrusion detection for network based cloud computing by custom RC-NN and optimization
title_short	Intrusion detection for network based cloud computing by custom RC-NN and optimization
title_full	Intrusion detection for network based cloud computing by custom RC-NN and optimization
title_fullStr	Intrusion detection for network based cloud computing by custom RC-NN and optimization
title_full_unstemmed	Intrusion detection for network based cloud computing by custom RC-NN and optimization
title_sort	intrusion detection for network based cloud computing by custom rc-nn and optimization
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/48605cf8490b455d837408d0b21e3dda
work_keys_str_mv	AT tthilagam intrusiondetectionfornetworkbasedcloudcomputingbycustomrcnnandoptimization AT raruna intrusiondetectionfornetworkbasedcloudcomputingbycustomrcnnandoptimization
_version_	1718406799336407040

Intrusion detection for network based cloud computing by custom RC-NN and optimization

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