Enhancing Network Intrusion Recovery in SDN with machine learning: an innovative approach

Abstract

In modern network environments, the swift recovery of network flow intrusions poses a substantial challenge. Particularly in the context of Software-Defined Networks (SDN), addressing this challenge necessitates the strategic selection of backup paths based on traffic patterns. In response to this critical issue, our paper introduces a groundbreaking approach known as Machine Learning-based Network Intrusion Recovery (MLBNIR) for enhancing intrusion recovery in SDN. We leverage a dedicated SDN dataset to train a flow-based Machine Learning (ML) model, enabling a deeper understanding of traffic dynamics within the SDN framework. Our study, presented in this paper, reveals that the MLBNIR approach significantly reduces intrusion recovery time by up to 90% and concurrently increases network bandwidth consumption by up to 57% when compared to existing methods reviewed in the literature.