arXiv AI July 13, 2026 By Iakovos-Christos Zarkadis, Christos Douligeris

Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation

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).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 3

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

arXiv:2607. 01305v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments.

By Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar, Qixu Gong, Wenbin Jiang, Huiping Cao, Satyajayant Misra, Jayashree Harikumar

llms diffusion benchmarks safety data-engineering

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

llms diffusion safety

arXiv AI

4d ago

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

arXiv:2609.36167v1 Announce Type: cross Abstract: Distributed Denial of Service attacks are a growing threat to network infrastructure, and new techniques, including the use of generative AI, make th...

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

diffusion benchmarks safety

arXiv Machine Learning

Jul 21

A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic

arXiv:2607. 17105v1 Announce Type: cross Abstract: It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks.

By Khushnaseeb Roshan

safety

arXiv AI

Jul 21

Boundary-Seeking GAN-Augmented TabTransformer for Adversarially Robust Intrusion Detection

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.

By Raihan Sultan Pasha Basuki, Aliyah Kurniasih

diffusion safety

arXiv AI

4d ago

Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation

arXiv:2509.20411v3 Announce Type: replace-cross Abstract: Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act a...

By Tharcisse Ndayipfukamiye, Jianguo Ding, Doreen Sebastian Sarwatt, Adamu Gaston Philipo, Huansheng Ning

llms diffusion benchmarks safety

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Related stories

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

A GAN-Based Framework for Robust DDoS Attack Detection

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

A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic

Boundary-Seeking GAN-Augmented TabTransformer for Adversarially Robust Intrusion Detection

Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation