Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.