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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 …