{
  "id": 10035,
  "url": "https://doi.org/10.3390/s26020363",
  "title": "Performance Analysis of Explainable Deep Learning-Based Intrusion Detection Systems for IoT Networks: A Systematic Review",
  "summary": "The opaque nature of black-box deep learning (DL) models poses significant challenges for intrusion detection systems (IDSs) in Internet of Things (IoT) networks, where transparency, trust, and operational reliability are critical. Although explainable artificial intelligence (XAI) has been increasingly adopted to enhance interpretability, its impact on detection performance and computational efficiency in resource-constrained IoT environments remains insufficiently understood. This systematic r",
  "authors": "Taiwo Blessing Ogunseyi, G Thiyagarajan, Honggang He, Vinay Bist, Zhengcong Du",
  "category": "research",
  "topics": "safety-alignment,transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-01-06T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:34:04.105Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/10035",
  "original_url": "https://doi.org/10.3390/s26020363",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}