Northampton Electronic Collection of Theses and Research

Convolutional Neural Network based algorithm for Early Warning Proactive System security in Software Defined Networks

Janabi, A., Kanakis, T. and Johnson, M. (2022) Convolutional Neural Network based algorithm for Early Warning Proactive System security in Software Defined Networks. IEEE Access. 10, 14301 - 14310. 2169-3536.

[img]

Janabi_etal_IEEEAccess_2022_Convolutional_Neural_Network_Based_Algorithm_for_Early_Warning_Proactive_Syste ... (1MB)

Item Type:	Article
Abstract:	Software-Defined Networking is an innovative architecture approach in the networking field. This technology allows networks to be centrally and intelligently managed by unified applications such as traffic classification and security management. Traditional networks’ static nature has a minimal capacity to meet organisations business requirements. Software-Defined Networks (SDNs) are the emerging architectures that address a range of networking challenges with new solutions. Nevertheless, these centralised and programmable techniques face various challenges and issues that require contemporary security solutions such as Intrusion Detection Systems. Recently, the majority of this type of security solution has been developed using Machine Learning techniques. Deep Learning algorithms have recently been used to provide more accuracy and efficiency. This paper presents a new detection approach based on Convolutional Neural Network (CNN). The experiments proved that the proposed model could be successfully implemented in a Software-Defined Network controller to detect various attacks with 100% accuracy, achieved a low degradation rate of 2.3% throughput and 1.8% latency when executed in a large-scale network.
Uncontrolled Keywords:	Deep Learning-Early Warning Proactive System (DL-EWPS), Convolutional Neural Network (CNN), Software-Defined Networking (SDN), Intrusion Detection System (IDS), Deep Learning (DL), RGB image, InSDN dataset
Creators:	Janabi, Ahmed, Kanakis, Triantafyllos and Johnson, Mark
Date:	31 January 2022
Date Type:	Publication
Page Range:	14301 - 14310
Journal or Publication Title:	IEEE Access
Volume:	10
Number of Pages:	10
Language:	English
DOI:	https://doi.org/10.1109/ACCESS.2022.3148134
ISSN:	2169-3536
Status:	Published / Disseminated
Refereed:	Yes
Related URLs:	http://aseados.ucd.ie/datasets/SDN https://pure.northampton.ac.uk/en/public...
URI:	http://nectar.northampton.ac.uk/id/eprint/16137

Actions (login required)

Edit Item

Edit Item

Altmetric

Accessibility Statement