Intrusion Detection Using Fuzzy Meta-Heuristic Approaches

Bachir Bahamida and Dalila Boughaci
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Bachir Bahamida: Laboratory of Research in Artificial Intelligence, Alger, Algeria
Dalila Boughaci: Laboratory of Research in Artificial Intelligence, Alger, Algeria

International Journal of Applied Metaheuristic Computing (IJAMC), 2014, vol. 5, issue 2, 39-53

Abstract: Due to a growing number of intrusion events, organizations are increasingly implementing various intrusion detection systems that classify network traffic data as normal or anomaly. In this paper, three intrusion detection systems based fuzzy meta-heuristics are proposed. The first one is a fuzzy stochastic local search (FSLS). The second one is a fuzzy tabu search (FTS) and the third one is a fuzzy deferential evolution (FDE). These classifiers are built on a knowledge base modelled as a fuzzy rule “if-then”. The main purpose of these methods is to get the highest quality solutions by optimizing the fuzzy rules generation. The proposed classifiers FSLS, FTS and FDE are tested on the benchmark KDD'99 intrusion dataset and compared with some well-known existing techniques for intrusion detection. The results show the efficiency of the proposed approaches in the intrusion detection field.

Date: 2014
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International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

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