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A Deterministic Crowding Evolutionary Algorithm for Optimization of a KNN-based Anomaly Intrusion Detection System
F. de Toro-Negro, P. Garcìa-Teodoro, J.E. Diáz-Verdejo, G. Maciá-Fernandez
111 - 120
10.3233/978-1-58603-890-8-111
Frontiers in Artificial Intelligence and Applications
Volume 177: Applications of Data Mining in E-Business and Finance
Abstract

This paper addresses the use of an evolutionary algorithm for the optimization of a K-nearest neighbor classifier to be used in the implementation of an intrusion detection system. The inclusion of a diversity maintenance technique embodied in the design of the evolutionary algorithm enables us to obtain different subsets of features extracted from network traffic data that lead to high classification accuracies. The methodology has been preliminarily applied to the Denial of Service attack detection, a key issue in maintaining continuity of the services provided by business organizations.

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