A naturally inspired statistical intrusion detection model
Growing interest in computational models based on natural phenomena with biologically inspired techniques in recent years have been tangible. The use of immune mechanisms in intrusion detection is promising. In [1] we proposed a new IDS model based on the Artificial Immune System (AIS) and a stati...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
International Association of Computer Science and Information Technology
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30622/ http://psasir.upm.edu.my/id/eprint/30622/1/A%20naturally%20inspired%20statistical%20intrusion%20detection%20model.pdf |
| _version_ | 1848846730361044992 |
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| author | Mahboubian, Mohammad Udzir, Nur Izura |
| author_facet | Mahboubian, Mohammad Udzir, Nur Izura |
| author_sort | Mahboubian, Mohammad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Growing interest in computational models based on natural phenomena with biologically inspired techniques in recent years have been tangible. The use of immune mechanisms in intrusion detection is promising. In [1] we proposed a new
IDS model based on the Artificial Immune System (AIS) and a
statistical approach. In this paper we are going to enhance that model in terms of detection speed and detection rate as well as overall overload. In contrast with the work in [1] here we do not use the concept of clonal selection and we use binary detector sets which leads to lower overload and therefore higher performance. The model is examined with DARPA data set which is famous among IDS researchers. |
| first_indexed | 2025-11-15T09:07:21Z |
| format | Article |
| id | upm-30622 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:07:21Z |
| publishDate | 2013 |
| publisher | International Association of Computer Science and Information Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-306222015-10-07T07:54:38Z http://psasir.upm.edu.my/id/eprint/30622/ A naturally inspired statistical intrusion detection model Mahboubian, Mohammad Udzir, Nur Izura Growing interest in computational models based on natural phenomena with biologically inspired techniques in recent years have been tangible. The use of immune mechanisms in intrusion detection is promising. In [1] we proposed a new IDS model based on the Artificial Immune System (AIS) and a statistical approach. In this paper we are going to enhance that model in terms of detection speed and detection rate as well as overall overload. In contrast with the work in [1] here we do not use the concept of clonal selection and we use binary detector sets which leads to lower overload and therefore higher performance. The model is examined with DARPA data set which is famous among IDS researchers. International Association of Computer Science and Information Technology 2013-06 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30622/1/A%20naturally%20inspired%20statistical%20intrusion%20detection%20model.pdf Mahboubian, Mohammad and Udzir, Nur Izura (2013) A naturally inspired statistical intrusion detection model. International Journal of Computer Theory and Engineering, 5 (3). pp. 578-581. ISSN 1793-8201; ESSN: 1793-821X http://www.ijcte.org/index.php?m=content&c=index&a=show&catid=49&id=871 10.7763/IJCTE.2013.V5.753 |
| spellingShingle | Mahboubian, Mohammad Udzir, Nur Izura A naturally inspired statistical intrusion detection model |
| title | A naturally inspired statistical intrusion detection model |
| title_full | A naturally inspired statistical intrusion detection model |
| title_fullStr | A naturally inspired statistical intrusion detection model |
| title_full_unstemmed | A naturally inspired statistical intrusion detection model |
| title_short | A naturally inspired statistical intrusion detection model |
| title_sort | naturally inspired statistical intrusion detection model |
| url | http://psasir.upm.edu.my/id/eprint/30622/ http://psasir.upm.edu.my/id/eprint/30622/ http://psasir.upm.edu.my/id/eprint/30622/ http://psasir.upm.edu.my/id/eprint/30622/1/A%20naturally%20inspired%20statistical%20intrusion%20detection%20model.pdf |