Feature selection for intrusion detection using new multi-objective estimation of distribution algorithms

The manipulation of a large number of features has become a critical problem in Intrusion Detection Systems(IDS). Therefore, Feature Selection (FS) is integrated to select the significant features, in order to avoid the computational complexity, and improve the classification performance. In this paper, we present a new multi-objective feature selection algorithm MOEDAFS (Multi-Objective Estimation of Distribution Algorithms (EDA) for Feature Selection). The MOEDAFS is based on EDA and Mutual Information (MI). EDA is used to explore the search space and MI is integrated as a probabilistic model to guide the search by modeling the redundancy and relevance relations between features. Therefore, we propose four probabilistic models for MOEDAFS. MOEDAFS selects the better feature subsets (non-dominated solutions) that have a better detection accuracy and smaller number of features. MOEDAFS uses two objective functions (minimizing classification Error Rate (ER) and minimizing the Number of Features(NF)). In order to demonstrate the performance of MOEDAFS, a comparative study is designed by internal and external comparison on NSL-KDD dataset. Internal comparison is performed between the four versions of MOEDAFS. External comparison is organized against some well-known deterministic, metaheuristic, and multi-objective feature selection algorithms that have a single and Multi-solution. Experimental results demonstrate that MOEDAFS outperforms recent algorithms.