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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...

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Main Authors: Mahboubian, Mohammad, Udzir, Nur Izura
Format: Article
Language:English
Published: International Association of Computer Science and Information Technology 2013
Online Access:http://psasir.upm.edu.my/id/eprint/30622/1/A%20naturally%20inspired%20statistical%20intrusion%20detection%20model.pdf
http://psasir.upm.edu.my/id/eprint/30622/
http://www.ijcte.org/index.php?m=content&c=index&a=show&catid=49&id=871
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Mahboubian, Mohammad
Udzir, Nur Izura
spellingShingle Mahboubian, Mohammad
Udzir, Nur Izura
A naturally inspired statistical intrusion detection model
author_facet Mahboubian, Mohammad
Udzir, Nur Izura
author_sort Mahboubian, Mohammad
title A naturally inspired statistical intrusion detection model
title_short 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_sort naturally inspired statistical intrusion detection model
publisher International Association of Computer Science and Information Technology
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/30622/1/A%20naturally%20inspired%20statistical%20intrusion%20detection%20model.pdf
http://psasir.upm.edu.my/id/eprint/30622/
http://www.ijcte.org/index.php?m=content&c=index&a=show&catid=49&id=871
_version_ 1643830114493923328
score 13.211869

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