Feature subset selection using binary gravitational search algorithm for intrusion detection system

Abstract

Due to control different infrastructures of networked computers in cyber security, intrusion detection system has been an important task essentially. Today, an effective intrusion detection system utilizes computational methods as machine learning techniques to improve detection rate with lowest false positive rate; however large number of irrelevant features as an optimization problem decrease this rate. This study using Binary Search Gravitational Algorithm (BGSA) as a feature selection method decreases irrelevant features in KDD 99 intrusion detection data set in order to improve Multi-layer perceptron performance. Results show that significant and relevant features increase performance of intrusion detection system near to 100% with lowest computational cost. © 2013 Springer-Verlag.