RFA Reinforced Firefly Algorithm to Identify Optimal Feature Subsets for Network IDS

Abstract

Intrusion detection systems (IDS's) play a vital role in network security to prevent the unauthorized use of data over networks. The feature selection approach is an important paradigm to strengthen IDS systems. In this article, a reinforced firefly-based feature selection model is proposed. This model utilizes the firefly inspired optimizer to select the features and it combines filter-based and wrapper-based approaches to boost the optimizer approach of the significant feature subset. In addition to that, novel classifiers are used to validate the efficiency of the selected subset. The proposed work is tested on the KDD Cup99 data sets which include 41 different features. Experimental results convey that the proposed work outperforms in terms of better detection accuracy, FPR and F-score. Also, it achieves better classification accuracy and less computational complexity compared to other algorithms.