Feature Engineering on the Cybersecurity Dataset for Deployment on Software Defined Network

Abstract

These days, due to dependency on the fast-moving world's modern technology, the increasing use of smart devices and the internet affect network traffic. Many intrusion detection studies concentrate on feature selection or reduction because some of the features are not correlated with the target variable, and some are redundant, which results in a tedious detection process and decrease the performance of an intrusion detection system (IDS). Our purpose is not to use all the features available but to take only the essential features; therefore, the process can be effective and efficient. In this paper, we have applied feature reduction algorithms on the NSL-KDD dataset for choosing a different kind of combination of features based on importance, similarity, correlation as an input to five classification algorithms to evaluate and determine the best performing model to deploy on a Software Defined Network (SDN) to reduce the dimension of the selected features.