{ "@context": { "dcterms": "http://purl.org/dc/terms/", "dcmitype": "http://purl.org/dc/dcmitype/" }, "dcterms:identifier": [ "tag:aim-pro.eu,2026:oer/f40027e54d10", "https://arxiv.org/abs/2607.01305", "arxiv:2607.01305" ], "dcterms:title": [ "Generative AI and Federated Learning for Intrusion Detection Systems: A Survey" ], "dcterms:type": [ { "@id": "dcmitype:Text" }, "preprint" ], "dcterms:creator": [ "Jiefei Liu", "Abu Saleh Md Tayeen", "Pratyay Kumar", "Qixu Gong", "Wenbin Jiang", "Huiping Cao", "Satyajayant Misra", "Jayashree Harikumar" ], "dcterms:description": [ "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, synthetic traffic generation, data augmentation, data imputation, adversarial traffic generation, and IDS alert explanation. FL enables distributed IDS training without directly sharing local network traffic, making it suitable for privacy-sensitive and geographically distributed environments. This survey provides a structured review of generative AI and FL techniques for IDS. We first summarize representative IDS research directions, including adversarial machine learning, anomaly-based detection, IoT-oriented IDS, explainable IDS, and benchmark datasets. We then categorize generative AI applications in IDS according to model families and task objectives, covering autoencoder-based models, Generative Adversarial Networks (GANs), diffusion models, and Large Language Models (LLMs). Finally, we review emerging studies that integrate generative AI with FL-based IDS and discuss open challenges, including synthetic data quality, realistic traffic generation, dual-use adversarial risks, non-IID client distributions, communication-efficient model sharing, federated IDS benchmarking, and domain-specific LLMs for network security." ], "dcterms:subject": [ "cs.CR (arxiv)", "cs.AI (arxiv)", "cs.LG (arxiv)" ], "dcterms:language": [ "en" ], "dcterms:license": [ { "@id": "http://creativecommons.org/licenses/by/4.0/" } ], "dcterms:rights": [ "CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. 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all:\"foundations of artificial intelligence\" OR all:\"overview of AI\" OR all:\"understanding AI\" OR all:\"history of AI\" OR all:\"AI essentials\" OR all:\"AI terminology\" OR all:\"metaphors for AI\" OR all:\"AI fundamental concepts\" OR all:\"AI key concepts\" OR all:\"philosophy of AI\" OR all:\"critical AI literacy\")”. arXiv served the resource and is not asserted to be its publisher or author.", "Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it." ], "inferred": { "dcterms:references": [ { "value": { "@id": "https://doi.org/10.1109/tse.1987.232894", "title": "doi:10.1109/tse.1987.232894" }, "method": "inferred", "rule": "citation", "rule_version": "links/1", "evidence": { "links": [ { "how": "citation", "line": 421, "basis": "reference", "literal": "doi:10.1109/tse.1987.232894" } ], "kind": "citation" } }, { "value": { "@id": "https://doi.org/10.1016/j.comnet.2022.109073", "title": "doi:10.1016/j.comnet.2022.109073" }, 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