{
  "id": 3044,
  "url": "https://arxiv.org/abs/2606.31594v1",
  "title": "Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks",
  "summary": "The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to address issues related to their security and privacy. These devices are limited in resources, which complicates their security and the protection of the data that they manage. The paper aims to examine in",
  "authors": "Rana Alharbi, Chuadhry Mujeeb Ahmed",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-30T12:43:25.000Z",
  "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/3044",
  "original_url": "https://arxiv.org/abs/2606.31594v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}