{
  "abstract": "The rapid proliferation of Internet of Things (IoT) devices has intensified network security challenges. Simultaneously, growing computational demands raise critical concerns about energy consumption and environmental sustainability. This paper proposes a Green-Aware Intrusion Detection System (GA-IDS) integrating sustainability metrics with machine learning to balance detection performance and energy efficiency. The framework employs a lightweight convolutional neural network optimized for resource-constrained platforms with dynamic power management and adaptive computational scaling. A novel carbon-conscious scheduling algorithm dynamically adjusts model complexity based on real-time grid carbon intensity. Experimental evaluation on CICIDS2017, NSL-KDD, and ToN-IoT datasets demonstrates 98.7% detection accuracy with 43% energy reduction compared to traditional deep learning-based IDS. The carbon-aware scheduler reduces emissions by 58.7% during high-carbon grid conditions without compromising detection quality. Comprehensive ablation studies validate component contributions, and comparative analysis against ten state-of-the-art approaches confirms GA-IDS achieves the highest Green Efficiency Score (0.0796), demonstrating superior balance between security performance and environmental responsibility.",
  "abstract_shown": true,
  "affiliations": [
    "College of Computer Science and Engineering, University of Hafr Albatin, Hafar Albatin, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Abdullah Alshammari"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ce0znssj5811y.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0355453",
  "fulltext_url": null,
  "id": "ce0znssj5811y",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0355453",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1705|Network Security and Intrusion Detection"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Computer architecture"
    ],
    [
      "plos",
      "/Computer and information sciences/Neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Real time computing"
    ],
    [
      "plos",
      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Sustainability science"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Power distribution/Power grids"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ]
  ],
  "openalex": "W7222323736",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0355453&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/ce0znssj5811y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0355453",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0355453"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Green-aware intrusion detection systems: A sustainability-driven approach to network security",
  "title_text": "Green-aware intrusion detection systems: A sustainability-driven approach to network security",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/ce0znssj5811y",
  "venue": "PLOS One",
  "versions": []
}