Dubem Ezeh, and J. de Oliveira
An SDN controller-based framework for anomaly detection using a GAN ensemble algorithm
Of recent, a handful of machine learning techniques have been proposed to handle the task of intrusion detection with algorithms taking charge; these algorithms learn, from traffic flow examples, to distinguish between benign and anomalous network events. In this paper, we explore the use of a Generative Adversarial Network (GAN) ensemble to detect anomalies in a Software-Defined Networking (SDN) environment using the Global Environment for Network Innovations (GENI) testbed over geographically separated instances. A controllerbased framework is proposed, comprising several components across the detection chain. A bespoke dataset is generated, addressing three of the most popular contemporary network attacks and using an SDN perspective. Evaluation results show great potential for detecting a wide array of anomalies.
Please cite this paper the following way:
Dubem Ezeh, and J. de Oliveira, "An SDN controller-based framework for anomaly detection using a GAN ensemble algorithm", Infocommunications Journal, Vol. XV, No 2, June 2023, pp. 29-36., https://doi.org/10.36244/ICJ.2023.2.5