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
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