Journal article
An SDN controller-based framework for anomaly detection using a GAN ensemble algorithm
INFOCOMMUNICATIONS JOURNAL, v 15(2)
2023
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 controller-based 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.
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Details
- Title
- An SDN controller-based framework for anomaly detection using a GAN ensemble algorithm
- Publication Details
- INFOCOMMUNICATIONS JOURNAL, v 15(2)
- Publisher
- SCIENTIFIC ASSOC INFOCOMMUNICATIONS; BUDAPEST
- Grant note
- Results presented in this paper were obtained using the Chameleon and GENI testbeds supported by the National Science Foundation.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:001072184400006
- Scopus ID
- 2-s2.0-85166957455
- Other Identifier
- 991021860619404721
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- Web of Science research areas
- Telecommunications