Citation
Mahboubian, Mohammad and Abdul Hamid, Nor Asila Wati
(2013)
A machine learning based AIS IDS.
International Journal of Machine Learning and Computing, 3 (3).
pp. 259-262.
ISSN 2010-3700
Abstract
In recent years we have seen a very great interest in combining naturally inspired techniques with existing conventional approaches. In this study we combined Negative Selection theory, one of most important theories in AIS, and knowledge production rules to propose a novel IDS. To generate the detectors first we produced a set of basic rules using knowledge production techniques with the help of WEKA, next the new detectors was generated and matured inside negative selection module and the basic rules. After experimenting the proposed model using DARAP 1999 dataset, this model showed a good performance compared to our previous models.
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Additional Metadata
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Computer Science and Information Technology |
| Publisher: | IACSIT Press |
| Keywords: | Intrusion detection; Artificial immune system; Negative selection; Data mining; Machine learning; WEKA. |
| Depositing User: | Ms. Nida Hidayati Ghazali |
| Date Deposited: | 03 Jun 2014 08:11 |
| Last Modified: | 07 Oct 2015 07:52 |
| URI: | http://psasir.upm.edu.my/id/eprint/30694 |
| Statistic Details: | View Download Statistic |
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