Desinging Intrusion Detection System in Software Defined Networks Using Hybrid Gwo-Ae-Rf Model

Abstract

Software Defined Networks is a promising networking solution which mitigates the limitations of traditional networks. The presence of logically centralized controller enables global view of the network in SDN. The provision for configuration and management of networking devices with high-level programming languages helps for adding proactive attack detection and mitigation strategies. However, SDN is prone to several evolving network attacks. Malicious traffic from botnets disrupts the network services and causes financial and reputational damages to individuals as well as enterprises. Intrusion detection systems aim to safeguard the network from vulnerabilities by detecting them instantaneously. Machine Learning based intrusion detection systems are used in traditional networks and are found very effective. This paper aims to build an Intrusion Detection System for Software Defined Networks leveraging machine learning and deep learning techniques. In order to build a model with reduced space and time complexity, feature selection and dimensionality reduction techniques were used. The feature selection using Grey Wolf Optimizer and dimensionality reduction with Autoencoder (GWO -AE) are incorporated in the study. The work is evaluated on the latest public SDN dataset -InSDN. Multiclass classification using Random Forest classifier with the reduced feature space obtained from GWO -AE gave weighted F1 score of 98.95%.