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Enhancing Network Intrusion Recovery in SDN with machine learning: an innovative approach.
- Source :
- Arab Journal of Basic & Applied Sciences; Dec2023, Vol. 30 Issue 1, p561-572, 12p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 25765299
- Volume :
- 30
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Arab Journal of Basic & Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 174339871
- Full Text :
- https://doi.org/10.1080/25765299.2023.2261219