A Hybrid Framework for the Sensitivity Analysis of Software-Defined Networking Performance Metrics Using Design of Experiments and Machine Learning Techniques

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Detalles Bibliográficos
Publicado en:	Information vol. 16, no. 9 (2025), p. 783-821
Autor principal:	Chekwube, Ezechi
Otros Autores:	Akinsolu, Mobayode O, Wilson, Sakpere, Sangodoyin, Abimbola O, Uyoata, Uyoata E, Owusu-Nyarko Isaac, Akinsolu, Folahanmi T
Publicado:	MDPI AG
Materias:	Software Design of experiments Classification Internet of Things Network topologies Sensitivity analysis Artificial neural networks Optimization Generative adversarial networks Hypercubes Machine learning Robustness Business metrics Parameter identification Data augmentation Performance measurement Artificial intelligence Neural networks Software-defined networking Denial of service attacks Quality of service Regression analysis Anomalies Real time Cybersecurity Synthetic data Latin hypercube sampling
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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