dc.contributor.author |
Isong, B
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|
dc.contributor.author |
Ratanang, T
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|
dc.contributor.author |
Gasela, N
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|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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dc.date.accessioned |
2024-03-15T08:54:11Z |
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dc.date.available |
2024-03-15T08:54:11Z |
|
dc.date.issued |
2023-11 |
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dc.identifier.citation |
Isong, B., Ratanang, T., Gasela, N. & Abu-Mahfouz, A.M. 2023. Integrated framework for enhancing SDN security and reliability. http://hdl.handle.net/10204/13635 . |
en_ZA |
dc.identifier.isbn |
979-8-3503-2781-6 |
|
dc.identifier.uri |
DOI: 10.1109/ICECET58911.2023.10389277
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dc.identifier.uri |
http://hdl.handle.net/10204/13635
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dc.description.abstract |
This paper addresses the issues of fault tolerance (FT) and intrusion detection (ID) in the Software-defined networking (SDN) environment. We design an integrated model that combines the FT-Manager as an FT mechanism and an ID-Manager, as an ID technique to collaboratively detect and mitigate threats in the SDN. The ID-Manager employs a machine learning (ML) technique to identify anomalous traffic accurately and effectively. Both techniques in the integrated model leverage the controllerswitches communication for real-time network statistics collection. While the full implementation of the framework is yet to be realized, experimental evaluations have been conducted to identify the most suitable ML algorithm for ID-Manager to classify network traffic using a benchmarking dataset and various performance metrics. The principal component analysis method was utilized for feature engineering optimization, and the results indicate that the Random Forest (RF) classifier outperforms other algorithms with 99.9% accuracy, precision, and recall. Based on these findings, the paper recommended RF as the ideal choice for ID design in the integrated model. We also stress the significance and potential benefits of the integrated model to sustain SDN network security and dependability. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.icecet.com/ |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/10389277 |
en_US |
dc.source |
International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 November 2023 |
en_US |
dc.subject |
Software-Defined Networking |
en_US |
dc.subject |
SDN |
en_US |
dc.subject |
Fault tolerance |
en_US |
dc.subject |
Intrusion detection |
en_US |
dc.subject |
Integrated model |
en_US |
dc.title |
Integrated framework for enhancing SDN security and reliability |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
8 |
en_US |
dc.description.note |
©2023 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/10389277 |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Isong, B., Ratanang, T., Gasela, N., & Abu-Mahfouz, A. M. (2023). Integrated framework for enhancing SDN security and reliability. http://hdl.handle.net/10204/13635 |
en_ZA |
dc.identifier.chicagocitation |
Isong, B, T Ratanang, N Gasela, and Adnan MI Abu-Mahfouz. "Integrated framework for enhancing SDN security and reliability." <i>International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 November 2023</i> (2023): http://hdl.handle.net/10204/13635 |
en_ZA |
dc.identifier.vancouvercitation |
Isong B, Ratanang T, Gasela N, Abu-Mahfouz AM, Integrated framework for enhancing SDN security and reliability; 2023. http://hdl.handle.net/10204/13635 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Isong, B
AU - Ratanang, T
AU - Gasela, N
AU - Abu-Mahfouz, Adnan MI
AB - This paper addresses the issues of fault tolerance (FT) and intrusion detection (ID) in the Software-defined networking (SDN) environment. We design an integrated model that combines the FT-Manager as an FT mechanism and an ID-Manager, as an ID technique to collaboratively detect and mitigate threats in the SDN. The ID-Manager employs a machine learning (ML) technique to identify anomalous traffic accurately and effectively. Both techniques in the integrated model leverage the controllerswitches communication for real-time network statistics collection. While the full implementation of the framework is yet to be realized, experimental evaluations have been conducted to identify the most suitable ML algorithm for ID-Manager to classify network traffic using a benchmarking dataset and various performance metrics. The principal component analysis method was utilized for feature engineering optimization, and the results indicate that the Random Forest (RF) classifier outperforms other algorithms with 99.9% accuracy, precision, and recall. Based on these findings, the paper recommended RF as the ideal choice for ID design in the integrated model. We also stress the significance and potential benefits of the integrated model to sustain SDN network security and dependability.
DA - 2023-11
DB - ResearchSpace
DP - CSIR
J1 - International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 November 2023
KW - Software-Defined Networking
KW - SDN
KW - Fault tolerance
KW - Intrusion detection
KW - Integrated model
LK - https://researchspace.csir.co.za
PY - 2023
SM - 979-8-3503-2781-6
T1 - Integrated framework for enhancing SDN security and reliability
TI - Integrated framework for enhancing SDN security and reliability
UR - http://hdl.handle.net/10204/13635
ER -
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en_ZA |
dc.identifier.worklist |
27610 |
en_US |