| Título: | A Genetic Algorithm Based elucidation for improving Intrusion Detection through condensed feature set by KDD 99 data set |
| Autores: |
Kandeeban, S. Selvakani Rajesh, R.S. |
| Fecha: | 2011-11-18 |
| Publicador: | Information and knowledge management |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article Peer-reviewed Article info:eu-repo/semantics/publishedVersion |
| Tema: | No aplica |
| Descripción: | An Intrusion detection system's main aim is to identify the normal and intrusive activities. The objective of this paper is to incorporate Genetic algorithm with reduced feature set into the system to detect and classify intrusions from normal. The experiments and evaluations of the proposed method were done using KDD cup 99 data set. The Genetic algorithm is used to derive a set of rules from the reduced training data set, and the fitness function is employed to judge the quality of rules. Keywords: Genetic Algorithm, Detection Rate, Intrusion Detection System, Reduced Feature Set, KDD 99 data set. |
| Idioma: | Inglés |