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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.
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]
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Database: Engineering Source
Description
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]
ISSN:10745351
DOI:10.1002/dac.70532