Performance Analysis of SDN-Based Intrusion Detection Model with Feature Selection Approach

Abstract

Generally, there are two types of approaches available for the detection of networks attacks, namely signature based and anomaly based. In this work, we will analyze the performance of anomaly-based detection model in SDN with the help of some common machine learning algorithms and feature selection mechanisms. We construct a mechanism of machine learning model for an intrusion detection system and train the model with the NSL-KDD Data set using feature selection technique. In order to enhance the performance of the classifier, some feature selection methods have been applied as a prepossessing of the data set. We have used five feature selection methods, namely Info Gain, Gain Ratio, CFS Subset Evaluator, Symmetric Uncertainty, and Chi-square test. A full data set of 41 features and a reduced data set after applying feature selection method has been experimented. A data set with feature selection ensures the highest accuracy with Random Forest classifier using Gain Ratio feature selection Evaluator.