A machine learning based AIS IDS.
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 dete...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
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IACSIT Press
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30694/ http://psasir.upm.edu.my/id/eprint/30694/1/A%20machine%20learning%20based%20AIS%20IDS.pdf |
| _version_ | 1848846751088246784 |
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| author | Mahboubian, Mohammad Abdul Hamid, Nor Asila Wati |
| author_facet | Mahboubian, Mohammad Abdul Hamid, Nor Asila Wati |
| author_sort | Mahboubian, Mohammad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T09:07:41Z |
| format | Article |
| id | upm-30694 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T09:07:41Z |
| publishDate | 2013 |
| publisher | IACSIT Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-306942015-10-07T07:52:58Z http://psasir.upm.edu.my/id/eprint/30694/ A machine learning based AIS IDS. Mahboubian, Mohammad Abdul Hamid, Nor Asila Wati 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. IACSIT Press 2013-06 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30694/1/A%20machine%20learning%20based%20AIS%20IDS.pdf 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 http://www.ijmlc.org/list-37-1.html English |
| spellingShingle | Mahboubian, Mohammad Abdul Hamid, Nor Asila Wati A machine learning based AIS IDS. |
| title | A machine learning based AIS IDS. |
| title_full | A machine learning based AIS IDS. |
| title_fullStr | A machine learning based AIS IDS. |
| title_full_unstemmed | A machine learning based AIS IDS. |
| title_short | A machine learning based AIS IDS. |
| title_sort | machine learning based ais ids. |
| url | http://psasir.upm.edu.my/id/eprint/30694/ http://psasir.upm.edu.my/id/eprint/30694/ http://psasir.upm.edu.my/id/eprint/30694/1/A%20machine%20learning%20based%20AIS%20IDS.pdf |