Adversarial Debiasing of Machine Learning Models for Enhanced Network Security against DDoS Attacks
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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.
arXiv:2606. 28439v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy.
arXiv:2503. 17867v3 Announce Type: replace-cross Abstract: Distributed Denial of Service attacks represent an active cybersecurity research problem.
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.
arXiv:2603. 17717v4 Announce Type: replace-cross Abstract: Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS).
arXiv:2607. 16348v1 Announce Type: cross Abstract: Machine learning-based intrusion detection systems (IDSs) often suffer from class imbalance and vulnerability to adversarial attacks, leading to degraded detection performance and reduced robustness.