Network Intrusion Detection Using Genetic Clustering

Elizabeth Leon¹, Olfa Nasraoui¹, and Jonatan Gomez²

¹Department of Electrical & Computer Engineering, The University of Memphis
eleon@memphis.edu
onasraou@memphis.edu

²Universidad Nacional de Colombia
jgomez@memphis.edu

Abstract. We apply the Unsupervised Niche Clustering (UNC), a genetic niching technique for robust and unsupervised clustering, to the intrusion detection problem. Using the normal samples, UNC generates clusters sumarizing the normal space. These clusters can be characterized by fuzzy membership functions, that are later aggregated to determine a level of normality. Anomalies are identified by their low normality levels.

LNCS 3103, p. 1312 f.

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