International Society of Science and Applied Technologies |
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A Survey on Feature Selection for Intrusion Detection | ||||
Author | Richard Zuech
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Co-Author(s) | Taghi M. Khoshgoftaar
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Abstract | This study examines previous research works in applying feature selection to Intrusion Detection. Feature selection has proven to improve or maintain similar classification accuracy for Intrusion Detection Systems (IDSs) while improving classification efficiency. Wrapper-based, Filter-based, and Hybrid feature selection techniques are evaluated. Since Intrusion Detection can face Big Data challenges, the classification efficiencies provided by feature selection can reduce computational demands. Previous feature selection research has been too narrowly focused on older KDD Intrusion Detection data sets. Researchers need access to additional high quality data sets which are publicly available.
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Keywords | Intrusion detection, IDS, Feature selection, Feature Reduction, Cybersecurity, Security | |||
Article #: 21150 |
August 6-8, 2015 - Philadelphia, Pennsylvia, U.S.A. |