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Item
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Feature Selection for Anomaly-Based Intrusion Detection System Using Information Gain and Mutual Correlation
http://hdl.handle.net/20.500.12678/0000004291
http://hdl.handle.net/20.500.12678/00000042918dc4fc7f-5a09-4fde-a8ca-366435f0b0b0
2e343851-4f2c-4026-a4b3-9cb8b4414db5
Name / File | License | Actions |
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Publication type | ||||||
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Feature Selection for Anomaly-Based Intrusion Detection System Using Information Gain and Mutual Correlation | |||||
Language | en | |||||
Publication date | 2011-05-05 | |||||
Authors | ||||||
Hlaing, Thuzar | ||||||
Khine, May Aye | ||||||
Description | ||||||
To avoid high computational costs inidentifying intrusions by IDSs, the size of adataset needs to be reduced. Feature selection isconsidered a problem of global combinatorialoptimization in machine learning, which reducesthe number of features, removes irrelevant, noisyand redundant data, and results in acceptableclassification accuracy. This paper proposes acombine filter method by using IG (informationgain) and Mutual Correlation for featureselection in NSL-KDD dataset. IG was used toselect important feature subsets from all featuresin the NSL-KDD dataset. The resulted featuresset are combined with Mutual correlation to getthe optimal reduced features set. Tests are doneon NSL-KDD dataset which is improved versionof KDD-99 dataset. The results show that thenumber of selected features is reduced from 41 to14 and correlated 10 features. The proposedmethod not only reduces the number of the inputfeatures and memory and CPU time but alsoincreases the classification accuracy. | ||||||
Keywords | ||||||
Information Gain, NSL-KDD, Feature Selection | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/223 | |||||
Journal articles | ||||||
Ninth International Conference On Computer Applications (ICCA 2011) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |