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Model of security level classification for data in hybrid cloud computing

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dc.citedby7
dc.contributor.authorShakir M.en_US
dc.contributor.authorAbubakar A.en_US
dc.contributor.authorYousoff O.en_US
dc.contributor.authorWaseem M.en_US
dc.contributor.authorAl-Emran M.en_US
dc.contributor.authorid57057236900en_US
dc.contributor.authorid35178991300en_US
dc.contributor.authorid57190977401en_US
dc.contributor.authorid57192432529en_US
dc.contributor.authorid56593108000en_US
dc.date.accessioned2023-05-29T06:11:16Z
dc.date.available2023-05-29T06:11:16Z
dc.date.issued2016
dc.description.abstractOrganizations mainly rely on data and the mechanism of dealing with that data on cloud computing. Data in an organization has multi security levels, which is classified depending on nature of the data, and the impact of data on the organization. The security procedures which used for protecting data usually be complicated, and it had a direct and indirect influence on the usability level. This study aims to establish a model which has an ability to classify data dynamically according to the security form low till high levels. The security level classified it into five levels based on the policies and classification method. The purpose of classification is to apply a complex security procedure on data which has a high security level larger than data which has a low security level. It also has a potential to segregation an illegal data from the legal to support usability in system. Finally, several experiments have been conducted to evaluate the proposed approaches. Several experiments have been performed to empirically evaluate two feature selection methods (Chi-square (?2), information gain (IG)) and five classification methods (decision tree classifier, Support Vector Machine (SVM), Na�ve Bayes (NB), and K-Nearest Neighbor (KNN) and meta-classifier combination) for Legal Documents Filtering The results show that all classifiers perform better with the information gain feature selection methods than their results with Chi-Square feature selection method. Results also show that Support Vector Machine (SVM) outperforms achieve the best results among all individual classifiers. However, the proposed meta-classifiers method achieves the best results among all classification approaches. � 2005 - 2016 JATIT & LLS. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage141
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85006374982
dc.identifier.spage133
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85006374982&partnerID=40&md5=a786299f0ff4ea8b573c509cc110952d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22596
dc.identifier.volume94
dc.publisherAsian Research Publishing Networken_US
dc.sourceScopus
dc.sourcetitleJournal of Theoretical and Applied Information Technology
dc.titleModel of security level classification for data in hybrid cloud computingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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