arXiv Machine Learning September 17, 2026 By Makram Chehayeb, Walid Fahs, Amina Rizk, Rida Khatoun, Omran Berjawi

A GAN-Based Framework for Robust DDoS Attack Detection

Read the original on arXiv Machine Learning →

The paper introduces a GAN‑based framework for detecting DDoS attacks that are designed to evade traditional security systems. It combines Random Forests, Deep Neural Ensembles, and Transformer models trained on the CICDDoS2019 dataset with synthetic adversarial traffic generated by a WGAN‑GP. Experiments show that this hybrid training significantly improves detection accuracy and resilience against unseen adversarial traffic, and real‑world tests confirm its practical effectiveness.

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