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An Improved Ant-IS Algorithm for Intrusion Detection

An Improved Ant-IS Algorithm for Intrusion Detection

Amal Miloud-Aouidate, Ahmed Riadh Baba-Ali
Copyright: © 2014 |Volume: 5 |Issue: 1 |Pages: 14
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466652613|DOI: 10.4018/ijamc.2014010104
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MLA

Miloud-Aouidate, Amal, and Ahmed Riadh Baba-Ali. "An Improved Ant-IS Algorithm for Intrusion Detection." IJAMC vol.5, no.1 2014: pp.65-78. http://doi.org/10.4018/ijamc.2014010104

APA

Miloud-Aouidate, A. & Baba-Ali, A. R. (2014). An Improved Ant-IS Algorithm for Intrusion Detection. International Journal of Applied Metaheuristic Computing (IJAMC), 5(1), 65-78. http://doi.org/10.4018/ijamc.2014010104

Chicago

Miloud-Aouidate, Amal, and Ahmed Riadh Baba-Ali. "An Improved Ant-IS Algorithm for Intrusion Detection," International Journal of Applied Metaheuristic Computing (IJAMC) 5, no.1: 65-78. http://doi.org/10.4018/ijamc.2014010104

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Abstract

During recent years, the number of attacks on networks has dramatically increased. Consequently the interest in network intrusion detection has increased among the researchers. This paper proposes a clustering Ant-IS and an active Ant colony optimization algorithms for intrusion detection in computer networks. The goal of these algorithms is to extract a set of learning instances from the initial training dataset. The proposed algorithms are an improvement of the previously presented Ant-IS algorithm, used is pattern recognition. Results of experimental tests show that the proposed algorithms are capable of producing a reliable intrusion detection system.

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