Hierarchical feature selection in IDS
Generally, IDS use all the features in network packet to evaluate and look for intrusive patterns. This data contains redundant and some give false correlation. Thus, feature selection is required to address this issue. This study integrates a statistical approach called Rough Set and evolutionary c...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/25370/ |
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| Summary: | Generally, IDS use all the features in network packet to evaluate and look for intrusive patterns. This data contains redundant and some give false correlation. Thus, feature selection is required to address this issue. This study integrates a statistical approach called Rough Set and evolutionary computing approach called Particle Swarm to form a 2-tier structure of feature selection process. Experimental results show that feature subset proposed by Rough-DPSO gives better representation of data and they are robust. |
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