This research presents a specialized GAN framework designed to enhance cybersecurity threat detection through advanced network traffic augmentation. By integrating nine differentiable loss components inspired by bio-inspired metaheuristics (Firefly, Jellyfish Search, and Mantis Shrimp), the model resolves class imbalance while preserving critical attack signatures.
* An energy-aware adaptive attention mechanism reduces training energy consumption by 40% without sacrificing accuracy.
* Tested across seven benchmark datasets, the framework achieved a high average accuracy of 98.73%.
* The model demonstrated strong robustness against adversarial evasion attempts.
* An energy-aware adaptive attention mechanism reduces training energy consumption by 40% without sacrificing accuracy.
* Tested across seven benchmark datasets, the framework achieved a high average accuracy of 98.73%.
* The model demonstrated strong robustness against adversarial evasion attempts.
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