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Kyaukse University
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Mohnyin University
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Myanmar Institute of Information Technology
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Myanmar Maritime University
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National Management Degree College
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Naypyitaw State Academy
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Pathein University
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Sagaing University
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Sagaing University of Education
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Taunggyi University
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Technological University, Hmawbi
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Technological University (Kyaukse)
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Technological University Mandalay
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University of Computer Studies, Mandalay
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University of Computer Studies, Meikhtila
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University of Computer Studies Pathein
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University of Computer Studies, Taungoo
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University of Computer Studies, Yangon
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University of Dental Medicine Mandalay
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University of Dental Medicine, Yangon
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University of Information Technology
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University of Mandalay
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University of Medicine 1
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Yangon University of Education
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New Index
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Item
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A Network Intrusion Detection Model Using Fuzzy C4.5 Decision Tree
http://hdl.handle.net/20.500.12678/0000004477
http://hdl.handle.net/20.500.12678/0000004477f268a8b3-38d2-48a7-871c-c8e991d1f2b2
c5b95ee1-7695-4b8c-8551-78fad8af82c2
Name / File | License | Actions |
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Publication type | ||||||
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | A Network Intrusion Detection Model Using Fuzzy C4.5 Decision Tree | |||||
Language | en_US | |||||
Publication date | 2012-02-28 | |||||
Authors | ||||||
Hlaing, Thuzar | ||||||
Khine, May Aye | ||||||
Description | ||||||
With the growing rate of interconnections among computer systems, reliablenetwork communication is becoming a majorchallenge. Intrusion detection has emerged as asignificant field of research, because it is nottheoretically possible to set up a system with novulnerabilities. This paper purposes the use offuzzy logic to generate decision tree to classifythe intrusion data. Further, the fuzzy decisiontree is then converted to fuzzy rules. The fuzzydecision tree (C4.5) method is used the minimizemeasure of classification ambiguity for differentattributes. This method overcomes the sharpboundary problems; provide good accuracydealing with continuous attributes and predictionproblems. The experimental result is carried outby using 10% KDD Cup 99 benchmark networkintrusion detection dataset. | ||||||
Keywords | ||||||
Fuzzy Logic, Fuzzy C4.5, Fuzzy Rules, Intrusion Detection | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/2406 | |||||
Journal articles | ||||||
Tenth International Conference On Computer Applications (ICCA 2012) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |