Algorithm for tuning fuzzy network attack classifiers based on invasive weed optimization

The purpose of this work is to describe a hybrid approach for constructing intrusion detection systems that incorporates feature extraction algorithms and algorithms for tuning classifiers. In this paper, we construct the classification algorithm based on the invasive weed optimization algorithm and use the genetic algorithm (GA) to reduce the dimension of the feature space. The experimental results support the efficiency of the proposed approach for solving intrusion detection problems.