Quokka Swarm Optimized Clifford‐Steerable Dynamic Graph Attention Network for Wireless SDN Traffic Control.
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| Title: | Quokka Swarm Optimized Clifford‐Steerable Dynamic Graph Attention Network for Wireless SDN Traffic Control. |
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| Authors: | Bharathi, S. Kavitha1 (AUTHOR) konguskb@gmail.com, Mathivanan, M.2 (AUTHOR), Mohamedyaseen, A.3 (AUTHOR), Alagarsundaram, Poovendran4 (AUTHOR) |
| Source: | International Journal of Communication Systems. 7/25/2026, Vol. 39 Issue 11, p1-17. 17p. |
| Subjects: | Software-defined networking, Routing systems, Telecommunication traffic, Machine learning, Swarm intelligence, Graph neural networks, Computer network security, Denial of service attacks |
| Abstract: | Software‐defined wireless networks (SDWNs) have emerged as a groundbreaking approach to managing advanced network environments, enabling centralized control and dynamic traffic management to deliver optimal performance. The highly advanced distributed denial‐of‐service (DDoS) attacks and growing network demands are signs of constant scalability, latency control, and effective routing issues during high load. Given these difficulties, this paper proposes a new system to address the shortcomings of traditional approaches. QCD‐Net (Quokka‐optimized Clifford‐steerable dynamic graph network) for wireless SDN traffic control proposes a new comprehensive approach that starts with adequate data collection from the DDOS attack SDN dataset and stringent preprocessing using min–max Z‐score normalization to maintain homogeneity. The most important characteristics are acquired using the uncertainty‐aware decision transformer, which identifies the major traffic behaviors and uncertainties. The inputs are fed into a software‐defined networking (SDN) controller with a traffic‐predicting unit based on a Clifford‐steerable dynamic graph attention network, encompassing both spatial and temporal network behavior. Routing decisions are optimized by the traffic routing unit using Quokka swarm optimization for adaptive load balancing with low latency. The performance of experimental testing is excellent, with a prediction accuracy of 99.5, routing efficiency of 98.7, detection rates of 99.2, and overall network performance improvements of 99.0. In general, the QCD‐Net framework should be regarded as a significant improvement in network resilience, scale, and efficiency in a dynamic, attack‐susceptible environment, paving the way for further improvements in wireless SDN traffic management and laying the foundations for future adaptive network management solutions. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194811090 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Quokka Swarm Optimized Clifford‐Steerable Dynamic Graph Attention Network for Wireless SDN Traffic Control. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bharathi%2C+S%2E+Kavitha%22">Bharathi, S. Kavitha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> konguskb@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mathivanan%2C+M%2E%22">Mathivanan, M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mohamedyaseen%2C+A%2E%22">Mohamedyaseen, A.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alagarsundaram%2C+Poovendran%22">Alagarsundaram, Poovendran</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Communication+Systems%22">International Journal of Communication Systems</searchLink>. 7/25/2026, Vol. 39 Issue 11, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software-defined+networking%22">Software-defined networking</searchLink><br /><searchLink fieldCode="DE" term="%22Routing+systems%22">Routing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+traffic%22">Telecommunication traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+security%22">Computer network security</searchLink><br /><searchLink fieldCode="DE" term="%22Denial+of+service+attacks%22">Denial of service attacks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software‐defined wireless networks (SDWNs) have emerged as a groundbreaking approach to managing advanced network environments, enabling centralized control and dynamic traffic management to deliver optimal performance. The highly advanced distributed denial‐of‐service (DDoS) attacks and growing network demands are signs of constant scalability, latency control, and effective routing issues during high load. Given these difficulties, this paper proposes a new system to address the shortcomings of traditional approaches. QCD‐Net (Quokka‐optimized Clifford‐steerable dynamic graph network) for wireless SDN traffic control proposes a new comprehensive approach that starts with adequate data collection from the DDOS attack SDN dataset and stringent preprocessing using min–max Z‐score normalization to maintain homogeneity. The most important characteristics are acquired using the uncertainty‐aware decision transformer, which identifies the major traffic behaviors and uncertainties. The inputs are fed into a software‐defined networking (SDN) controller with a traffic‐predicting unit based on a Clifford‐steerable dynamic graph attention network, encompassing both spatial and temporal network behavior. Routing decisions are optimized by the traffic routing unit using Quokka swarm optimization for adaptive load balancing with low latency. The performance of experimental testing is excellent, with a prediction accuracy of 99.5, routing efficiency of 98.7, detection rates of 99.2, and overall network performance improvements of 99.0. In general, the QCD‐Net framework should be regarded as a significant improvement in network resilience, scale, and efficiency in a dynamic, attack‐susceptible environment, paving the way for further improvements in wireless SDN traffic management and laying the foundations for future adaptive network management solutions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/dac.70532 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Software-defined networking Type: general – SubjectFull: Routing systems Type: general – SubjectFull: Telecommunication traffic Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Computer network security Type: general – SubjectFull: Denial of service attacks Type: general Titles: – TitleFull: Quokka Swarm Optimized Clifford‐Steerable Dynamic Graph Attention Network for Wireless SDN Traffic Control. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bharathi, S. Kavitha – PersonEntity: Name: NameFull: Mathivanan, M. – PersonEntity: Name: NameFull: Mohamedyaseen, A. – PersonEntity: Name: NameFull: Alagarsundaram, Poovendran IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 07 Text: 7/25/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10745351 Numbering: – Type: volume Value: 39 – Type: issue Value: 11 Titles: – TitleFull: International Journal of Communication Systems Type: main |
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