An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems

Providing security to the Supervisory Control and Data Acquisition (SCADA) systems is one of the demanding and crucial tasks in recent days, due to the different types of attacks on the network. For this purpose, there are different types of attack detection and classification methodologies have bee...

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Autores principales:	S. Shitharth, Kantipudi Mvv Prasad, K. Sangeetha, Pravin R. Kshirsagar, Thanikanti Sudhakar Babu, Hassan Haes Alhelou
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Supervisory control and data acquisition (SCADA) Markov chain clustering (MCC) preprocessing security rapid probabilistic correlated optimization (RPCO) block correlated neural network (BCNN) and attack detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/df3cb54ac59f422588a496a0859fb7b3
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spelling	oai:doaj.org-article:df3cb54ac59f422588a496a0859fb7b32021-12-01T00:01:36ZAn Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems2169-353610.1109/ACCESS.2021.3129053https://doaj.org/article/df3cb54ac59f422588a496a0859fb7b32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9618916/https://doaj.org/toc/2169-3536Providing security to the Supervisory Control and Data Acquisition (SCADA) systems is one of the demanding and crucial tasks in recent days, due to the different types of attacks on the network. For this purpose, there are different types of attack detection and classification methodologies have been developed in the conventional works. But it limits with the issues like high complexity in design, misclassification results, increased error rate, and reduced detection efficiency. In order to solve these issues, this paper aims to develop an advanced machine learning models for improving the SCADA security. This work comprises the stages of preprocessing, clustering, feature selection, and classification. At first, the Markov Chain Clustering (MCC) model is implemented to cluster the network data by normalizing the feature values. Then, the Rapid Probabilistic Correlated Optimization (RPCO) mechanism is employed to select the optimal features by computing the matching score and likelihood of particles. Finally, the Block Correlated Neural Network (BCNN) technique is employed to classify the predicted label, where the relevancy score is computed by using the kernel function with the feature points. During experimentation, there are different performance indicators have been used to validate the results of proposed attack detection mechanisms. Also, the obtained results are compared with the RPCO-BCNN mechanism for proving the superiority of the proposed attack detection system.S. ShitharthKantipudi Mvv PrasadK. SangeethaPravin R. KshirsagarThanikanti Sudhakar BabuHassan Haes AlhelouIEEEarticleSupervisory control and data acquisition (SCADA)Markov chain clustering (MCC)preprocessingsecurityrapid probabilistic correlated optimization (RPCO)block correlated neural network (BCNN) and attack detectionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156297-156312 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Supervisory control and data acquisition (SCADA) Markov chain clustering (MCC) preprocessing security rapid probabilistic correlated optimization (RPCO) block correlated neural network (BCNN) and attack detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Supervisory control and data acquisition (SCADA) Markov chain clustering (MCC) preprocessing security rapid probabilistic correlated optimization (RPCO) block correlated neural network (BCNN) and attack detection Electrical engineering. Electronics. Nuclear engineering TK1-9971 S. Shitharth Kantipudi Mvv Prasad K. Sangeetha Pravin R. Kshirsagar Thanikanti Sudhakar Babu Hassan Haes Alhelou An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems
description	Providing security to the Supervisory Control and Data Acquisition (SCADA) systems is one of the demanding and crucial tasks in recent days, due to the different types of attacks on the network. For this purpose, there are different types of attack detection and classification methodologies have been developed in the conventional works. But it limits with the issues like high complexity in design, misclassification results, increased error rate, and reduced detection efficiency. In order to solve these issues, this paper aims to develop an advanced machine learning models for improving the SCADA security. This work comprises the stages of preprocessing, clustering, feature selection, and classification. At first, the Markov Chain Clustering (MCC) model is implemented to cluster the network data by normalizing the feature values. Then, the Rapid Probabilistic Correlated Optimization (RPCO) mechanism is employed to select the optimal features by computing the matching score and likelihood of particles. Finally, the Block Correlated Neural Network (BCNN) technique is employed to classify the predicted label, where the relevancy score is computed by using the kernel function with the feature points. During experimentation, there are different performance indicators have been used to validate the results of proposed attack detection mechanisms. Also, the obtained results are compared with the RPCO-BCNN mechanism for proving the superiority of the proposed attack detection system.
format	article
author	S. Shitharth Kantipudi Mvv Prasad K. Sangeetha Pravin R. Kshirsagar Thanikanti Sudhakar Babu Hassan Haes Alhelou
author_facet	S. Shitharth Kantipudi Mvv Prasad K. Sangeetha Pravin R. Kshirsagar Thanikanti Sudhakar Babu Hassan Haes Alhelou
author_sort	S. Shitharth
title	An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems
title_short	An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems
title_full	An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems
title_fullStr	An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems
title_full_unstemmed	An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems
title_sort	enriched rpco-bcnn mechanisms for attack detection and classification in scada systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/df3cb54ac59f422588a496a0859fb7b3
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An Enriched RPCO-BCNN Mechanisms for Attack Detection and Classification in SCADA Systems

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