Enhanced Intrusion Detection in Software-Defined Networking using Advanced Feature Selection: The EMRMR Approach

Abstract

Most traditional IP networks face serious security and management challenges due to their rapid increase in complexity. SDN resolves these issues by the separation of control and data planes, hence enabling programmability for centralized management with flexibility. On the other hand, its centralized architecture makes SDN very prone to DDoS attacks, hence necessitating the use of advanced and efficient IDSs. This study focuses on improving IDS performance in SDN environments through the integration of deep learning techniques and novel feature selection methods. This study presents an Enhanced Maximum Relevance Minimum Redundancy (EMRMR) approach that incorporates a Mutual Information Feature Selection (MIFS) strategy and a new Contextual Redundancy Coefficient Upweighting (CRCU) strategy to optimize feature selection for early attack detection. Experiments on the inSDN dataset showed that EMRMR achieved better precision, recall, F1-score, and accuracy compared to the state-of-the-art approaches, especially when fewer features are selected. These results highlight the efficiency of the proposed EMRMR approach in the selection of relevant features with minimal computational overhead, which enhances the real-time capability for IDS in SDN environments.