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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Benaicha, S.E. Saoudi, L. Bouhouita Guermeche, S.E. Lounis, O. |
| Copyright Year | 2014 |
| Description | Author affiliation: Comput. Sci. Dept., Univ. of Mohamed Boudiaf of M'Sila, M'Sila, Algeria (Benaicha, S.E.; Saoudi, L.; Bouhouita Guermeche, S.E.; Lounis, O.) |
| Abstract | In this paper, we present a Genetic Algorithm (GA) approach with an improved initial population and selection operator, to efficiently detect various types of network intrusions. GA is used to optimize the search of attack scenarios in audit files, thanks to its good balance exploration / exploitation; it provides the subset of potential attacks which are present in the audit file in a reasonable processing time. In the testing phase the Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD99) benchmark dataset has been used to detect the misuse activities. By combining the IDS with Genetic algorithm increases the performance of the detection rate of the Network Intrusion Detection Model and reduces the false positive rate. |
| Starting Page | 564 |
| Ending Page | 568 |
| File Size | 455117 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780989319331 |
| e-ISBN | 9780989319317 |
| DOI | 10.1109/SAI.2014.6918242 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-27 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | The Science and Information (SAI) Organization |
| Subject Keyword | Training NSL_KDD Sociology Intrusion detection system Genetic algorithm Intrusion detection Initial population Fitness function Statistics Biological cells Monitoring Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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