arXiv AI By Aadith Sukumar, Isha Singh, Devershika Mohane, Ankit Mukherjee, Ankush Dutta, Rahee Walambe, Ketan Kotecha

Adversarial Debiasing of Machine Learning Models for Enhanced Network Security against DDoS Attacks

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arXiv Machine Learning
Sep 17

A GAN-Based Framework for Robust DDoS Attack Detection

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.

By Makram Chehayeb, Walid Fahs, Amina Rizk, Rida Khatoun, Omran Berjawi
arXiv AI
Aug 19

Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks

The paper introduces GraphGAN, a Graph-based Generative Adversarial Network designed to detect Distributed Denial-of-Service (DDoS) attacks in next-generation networks. It transforms sequential traffic flows into k‑nearest neighbor graphs, uses a generator to create realistic minority samples, and employs Graph Convolutional Networks for both discrimination and final classification. Experiments on four benchmark datasets demonstrate that GraphGAN outperforms existing methods in accuracy, precision, and recall, especially when data are scarce.

By Mohammad Arif Hossain, Yeahia Sarker, Md Jafrin Hossain, Most. Humayra Khanom Rime, Nirwan Ansari