Rough Set Significant Reduct and Rules of Intrusion Detection System

Intrusion Detection System deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, also higher resource consumption as well as poor detection rate. It is not simply removing these irrelevant or redundant features due to deteriorate...

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Main Authors:	Noor Suhana, Sulaiman, Rohani, Abu Bakar
Format:	Article
Language:	English
Published:	Digital Information Research Foundation, India 2017
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/21202/ http://umpir.ump.edu.my/id/eprint/21202/ http://umpir.ump.edu.my/id/eprint/21202/1/jisrv8n1_3-1.pdf

id	ump-21202
recordtype	eprints
spelling	ump-212022018-05-25T07:02:59Z http://umpir.ump.edu.my/id/eprint/21202/ Rough Set Significant Reduct and Rules of Intrusion Detection System Noor Suhana, Sulaiman Rohani, Abu Bakar QA75 Electronic computers. Computer science Intrusion Detection System deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, also higher resource consumption as well as poor detection rate. It is not simply removing these irrelevant or redundant features due to deteriorate the performance of classifiers. Furthermore, by choosing the effective and important features, the classification mode and the classification performance will be improved. Rough Set is the most widely used as a baseline technique of single classifier approach on intrusion detection system. Typically, Rough Set is an efficient instrument in dealing with huge dataset in concert with missing values and granularing the features. However, large numbers of generated features reducts and rules must be chosen cautiously to reduce the processing power in dealing with massive parameters for classification. Hence, the primary objective of this study is to probe the significant reducts and rules prior to classification process of Intrusion Detection System. All embracing analyses are presented to eradicate the insignificant attributes, reduct and rules for better classification taxonomy. Reducts with core attributes and minimal cardinality are preferred to construct new decision table, and subsequently generate high classification rates. In addition, rules with highest support, fewer length, high Rule Importance Measure (RIM) and high coverage rule are favored since they reveal high quality performance. The results are compared in terms of the classification accuracy between the original decision table and a new decision table. Digital Information Research Foundation, India 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/21202/1/jisrv8n1_3-1.pdf Noor Suhana, Sulaiman and Rohani, Abu Bakar (2017) Rough Set Significant Reduct and Rules of Intrusion Detection System. Journal of Information Security Research, 8 (6). pp. 17-25. ISSN 0976-4151 http://www.dline.info/jisr/fulltext/v8n1/jisrv8n1_3.pdf
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
spellingShingle	QA75 Electronic computers. Computer science Noor Suhana, Sulaiman Rohani, Abu Bakar Rough Set Significant Reduct and Rules of Intrusion Detection System
description	Intrusion Detection System deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, also higher resource consumption as well as poor detection rate. It is not simply removing these irrelevant or redundant features due to deteriorate the performance of classifiers. Furthermore, by choosing the effective and important features, the classification mode and the classification performance will be improved. Rough Set is the most widely used as a baseline technique of single classifier approach on intrusion detection system. Typically, Rough Set is an efficient instrument in dealing with huge dataset in concert with missing values and granularing the features. However, large numbers of generated features reducts and rules must be chosen cautiously to reduce the processing power in dealing with massive parameters for classification. Hence, the primary objective of this study is to probe the significant reducts and rules prior to classification process of Intrusion Detection System. All embracing analyses are presented to eradicate the insignificant attributes, reduct and rules for better classification taxonomy. Reducts with core attributes and minimal cardinality are preferred to construct new decision table, and subsequently generate high classification rates. In addition, rules with highest support, fewer length, high Rule Importance Measure (RIM) and high coverage rule are favored since they reveal high quality performance. The results are compared in terms of the classification accuracy between the original decision table and a new decision table.
format	Article
author	Noor Suhana, Sulaiman Rohani, Abu Bakar
author_facet	Noor Suhana, Sulaiman Rohani, Abu Bakar
author_sort	Noor Suhana, Sulaiman
title	Rough Set Significant Reduct and Rules of Intrusion Detection System
title_short	Rough Set Significant Reduct and Rules of Intrusion Detection System
title_full	Rough Set Significant Reduct and Rules of Intrusion Detection System
title_fullStr	Rough Set Significant Reduct and Rules of Intrusion Detection System
title_full_unstemmed	Rough Set Significant Reduct and Rules of Intrusion Detection System
title_sort	rough set significant reduct and rules of intrusion detection system
publisher	Digital Information Research Foundation, India
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/21202/ http://umpir.ump.edu.my/id/eprint/21202/ http://umpir.ump.edu.my/id/eprint/21202/1/jisrv8n1_3-1.pdf
first_indexed	2023-09-18T22:31:01Z
last_indexed	2023-09-18T22:31:01Z
_version_	1777416285829726208

Rough Set Significant Reduct and Rules of Intrusion Detection System

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