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Evidence record 3036 · automatically gathered

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. Rec

Record details

Published: 1 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Privacy · Military & security · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (1 July 2026), “Generative AI and Federated Learning for Intrusion Detection Systems: A Survey,” evidence record 3036, https://ethics.ai/record/3036 (originally published by arXiv cs.CR (AI security)).

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