{
  "id": 19059,
  "url": "https://arxiv.org/abs/2608.11291v1",
  "title": "Dueling Deep Q-Learning for Intrusion Detection",
  "summary": "Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new attack types. This study proposes a novel approach by employing a reward-based, dueling Q-learning model for IDS, achieving an average accuracy of 99.68% across multiple attack classes. The proposed model has a dueling network architecture which separates its predictions",
  "authors": "Logan Luna, Matthew P. Berkowitz, Laxima Niure Kandel, Sirio Jansen-S'anchez",
  "category": "research",
  "topics": "military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T16:55:00.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "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/19059",
  "original_url": "https://arxiv.org/abs/2608.11291v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}