I. A. Hodashinsky, M. A. Mech
Department of Complex Information Security Tomsk State University of Control
Systems and Radioelectronics (TUSUR), Tomsk, Russia
ABSTRACT
This paper presents a method for constructing intrusion detection systems based on efficient fuzzy rulebased
classifiers. The design process of a fuzzy rule-based classifier from a given input-output data set can
be presented as a feature selection and parameter optimization problem. For parameter optimization of
fuzzy classifiers, the differential evolution is used, while the binary harmonic search algorithm is used for
selection of relevant features. The performance of the designed classifiers is evaluated using the KDD Cup
1999 intrusion detection dataset. The optimal classifier is selected based on the Akaike information
criterion. The optimal intrusion detection system has a 1.21% type I error and a 0.39% type II error. A
comparative study with other methods was accomplished. The results obtained showed the adequacy of the
proposed method.
KEYWORDS
Intrusion detection; fuzzy classifier; differential evolution; feature selection; binary harmonic search; Akaike information criterion
No comments:
Post a Comment