Weighted heterogeneous ensemble for the classification of intrusion detection using ant colony optimization for continuous search spaces

This paper proposes a heterogeneous ensemble classifier configuration for a multiclass intrusion detection problem. The ensemble is composed of k-nearest neighbors, artificial neural networks, and naïve Bayes classifiers. The decisions of these classifiers are combined with weighted majority voting, where optimal weights are generated by ant colony optimization for continuous search spaces. As a comparison basis, we have also implemented the ensemble configuration with the unweighted majority voting or Winner Takes All strategy. To ensure the maximum variety of classifiers, we have implemented three versions of each classification algorithm by varying each classifier’s parameters making a total of nine diverse experts for the ensemble. For our empirical study, we used the full NSL-KDD dataset to classify network traffic into one of five different classes. Our results indicate that the ensemble configuration using ACOR-optimized weights is capable of resolving the conflicts between multiple classifiers and improving the overall classification accuracy of the ensemble.