{
  "id": 3036,
  "url": "https://arxiv.org/abs/2607.01305v1",
  "title": "Generative AI and Federated Learning for Intrusion Detection Systems: A Survey",
  "summary": "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",
  "authors": "Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar, Qixu Gong, Wenbin Jiang, Huiping Cao, Satyajayant Misra, Jayashree Harikumar",
  "category": "research",
  "topics": "privacy-surveillance,military-security,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-01T16:37:49.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/3036",
  "original_url": "https://arxiv.org/abs/2607.01305v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}