An Intrusion Detection System for SDN Using Machine Learning

Abstract

Software Defined Networking (SDN) has emerged as a promising and exciting option for the future growth of the internet. SDN has increased the flex-ibility and transparency of the managed, centralized, and controlled network. On the other hand, these advantages create a more vulnerable environment with sub-stantial risks, culminating in network difficulties, system paralysis, online banking frauds, and robberies. These issues have a significant detrimental impact on orga-nizations, enterprises, and even economies. Accuracy, high performance, and real-time systems are necessary to achieve this goal. Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System (IDS) has sti-mulated the interest of numerous research investigators over the last decade. In this paper, a novel HFS-LGBM IDS is proposed for SDN. First, the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset. In the first phase, the Correlation based Feature Selection (CFS) algorithm is used to obtain the feature subset. The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination (RF-RFE) in the second phase. A LightGBM algorithm is then used to detect and classify different types of attacks. The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy, precision, recall and f-measure.