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An explainable machine learning-based framework for accurate, transparent, and reliable DDoS attack detection in an SDN environment that combines reliable DDoS detection with transparent, analyst-oriented decision support for SDN security monitoring is developed.
The need for better-aligned multimodal datasets, standardized evaluation protocols, lightweight fusion architectures, and robust models capable of operating in dynamic real-world environments is highlighted.
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