• Mar 05, 2019 News!Vol.11, No.1 has been published with online version.   [Click]
  • Aug 06, 2018 News!Vol.9, No.1-Vol.9, No.4 have been indexed by EI (Inspec).   [Click]
  • Dec 29, 2018 News!Vol.10, No.6 has been published with online version.   [Click]
General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2013 Vol.5(3): 578-581 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2013.V5.753

A Naturally Inspired Statistical Intrusion Detection Model

M. Mahboubian and Nor I. Udzir
Abstract—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.

Index Terms—Intrusion detection, artificial immune system, negative selection, data mining, network security.

The authors are with Faculty of Computer Science and Information Technology University Putra Malaysia, Kuala Lumpur, Malaysia (e-mail: Mahboubian.uni@gmail.com, izura@fsktm.upm.edu.my).


Cite:M. Mahboubian and Nor I. Udzir, "A Naturally Inspired Statistical Intrusion Detection Model," International Journal of Computer Theory and Engineering vol. 5, no. 3, pp. 578-581, 2013.

Copyright © 2008-2019. International Journal of Computer Theory and Engineering. All rights reserved.
E-mail: ijcte@iacsitp.com