A Deterministic Crowding Evolutionary Algorithm for Optimization of a KNN-based Anomaly Intrusion Detection System

Authors:
F. de Toro-Negro;P. Garcìa-Teodoro;J. E. Diáz-Verdejo;G. Maciá-Fernandez
Affiliations:
Signal Theory, Telematics and Communications Department, University of Granada, Spain;Signal Theory, Telematics and Communications Department, University of Granada, Spain;Signal Theory, Telematics and Communications Department, University of Granada, Spain;Signal Theory, Telematics and Communications Department, University of Granada, Spain
Venue:
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Year:
2008

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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.