Feature selection and multiple classifier fusion using genetic algorithms in intrusion detection systems

Abstract

With the improvements in information systems, intrusion detection systems (IDS) become more important. IDS can be thought as a classification problem. An important step of classification applications is feature selection step. Nowadays, to improve accuracy of classifiers, it is recommended to use classifier fusion instead of single classifiers. This study proposes to use genetic algorithms for both feature selection and weight selection for classifier fusion in IDS. This proposed system called GA-NS-AB, has been applied to NSL-KDD dataset. Number of classifiers used in fusion changes between 2 and 8. Following classifiers have been used: Adaboost, Decision Tree, Logistic Regression, Naive Bayes, Random Forests, Gradient Boosting, K-Nearest Neighbor, and Neural Networks Multi-Layer Perceptron. The results of the proposed method have been compared with simple voting and probability voting fusion methods and single classifiers. In addition, GA-NS-AB is also compared with previous results. GA-NS-AB is a high accuracy classifier fusion that reduces test and training time.