Generative AI and Federated Learning for Intrusion Detection Systems: A Survey
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. However, developing reliable IDS models remains challenging because attack behaviors evolve over time, realistic datasets are difficult to obtain, traffic records may be incomplete, attack classes are often imbalanced, and privacy constraints limit centralized data collection. Recent advances in generative artificial intelligence (AI) and Federated Learning (FL) provide new opportunities to address these limitations. Generative models can support anomaly detection, syntheti...
| Authors | Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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