An SDN controller-based framework for anomaly detection using a GAN ensemble algorithm
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Published:2023
Issue:2
Volume:15
Page:29-36
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ISSN:2061-2079
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Container-title:Infocommunications journal
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language:
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Short-container-title:Infocommunications journal
Author:
Ezeh Dubem A.,de Oliveira Jaudelice
Abstract
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.
Publisher
Infocommunications Journal
Subject
Electrical and Electronic Engineering,General Computer Science