SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network

The ability of cybercriminals tohide their intentionto attack obstructs existingprotectionsystems causing the system to be unable to prevent any possible sabotage in network systems. In this paper, we propose a Similarity approach for Attack Intention Recognition using Fuzzy Min-Max Neural Network (...

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Main Authors:	Ahmed, Abdulghani Ali, Mohammed, Mohammed Falah
Format:	Article
Language:	English
Published:	Elsevier 2018
Subjects:	QA75 Electronic computers. Computer science T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/1/SAIRF%20A%20similarity%20approach%20for%20attack%20intention%20recognition.pdf

id	ump-23820
recordtype	eprints
spelling	ump-238202019-01-17T01:21:53Z http://umpir.ump.edu.my/id/eprint/23820/ SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network Ahmed, Abdulghani Ali Mohammed, Mohammed Falah QA75 Electronic computers. Computer science T Technology (General) The ability of cybercriminals tohide their intentionto attack obstructs existingprotectionsystems causing the system to be unable to prevent any possible sabotage in network systems. In this paper, we propose a Similarity approach for Attack Intention Recognition using Fuzzy Min-Max Neural Network (SAIRF). In particular, the proposed SAIRF approach aims to recognize attack intention in real time. This approach classifies attacks according to their characteristics and uses similar metric method to identify motives of attacks and predict their intentions. In this study, network attack intentions are categorized into specific and general intentions. General intentions are recognized by investigating violations against the security metrics of confidentiality, integrity, availability, and authenticity. Specific intentions are recognized by investigating the network attacks used to achieve a violation. The obtained results demonstrate the capability of the proposed approach to investigate similarity of network attack evidence and recognize the intentions of the attack being investigated Elsevier 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23820/1/SAIRF%20A%20similarity%20approach%20for%20attack%20intention%20recognition.pdf Ahmed, Abdulghani Ali and Mohammed, Mohammed Falah (2018) SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network. Journal of Computational Science, 25. pp. 467-473. ISSN 1877-7503 http: www.elsevier.com/locate/jocs https://doi.org/10.1016/j.jocs.2017.09.007
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science T Technology (General)
spellingShingle	QA75 Electronic computers. Computer science T Technology (General) Ahmed, Abdulghani Ali Mohammed, Mohammed Falah SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network
description	The ability of cybercriminals tohide their intentionto attack obstructs existingprotectionsystems causing the system to be unable to prevent any possible sabotage in network systems. In this paper, we propose a Similarity approach for Attack Intention Recognition using Fuzzy Min-Max Neural Network (SAIRF). In particular, the proposed SAIRF approach aims to recognize attack intention in real time. This approach classifies attacks according to their characteristics and uses similar metric method to identify motives of attacks and predict their intentions. In this study, network attack intentions are categorized into specific and general intentions. General intentions are recognized by investigating violations against the security metrics of confidentiality, integrity, availability, and authenticity. Specific intentions are recognized by investigating the network attacks used to achieve a violation. The obtained results demonstrate the capability of the proposed approach to investigate similarity of network attack evidence and recognize the intentions of the attack being investigated
format	Article
author	Ahmed, Abdulghani Ali Mohammed, Mohammed Falah
author_facet	Ahmed, Abdulghani Ali Mohammed, Mohammed Falah
author_sort	Ahmed, Abdulghani Ali
title	SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network
title_short	SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network
title_full	SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network
title_fullStr	SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network
title_full_unstemmed	SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network
title_sort	sairf: a similarity approach for attack intention recognition using fuzzy min-max neural network
publisher	Elsevier
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/1/SAIRF%20A%20similarity%20approach%20for%20attack%20intention%20recognition.pdf
first_indexed	2023-09-18T22:35:51Z
last_indexed	2023-09-18T22:35:51Z
_version_	1777416589978632192

SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network

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