{
  "abstract": "The Industrial Internet of Things (IIoT) faces severe intrusion-detection challenges under multi-protocol, multi-device, and massive heterogeneous traffic, especially extreme class imbalance and poor minority-class detection. To address this, we propose a fuzzy-clustering-based multi-scale intrusion detection model (FPBW-IDS). At the data level, we introduce boundary-prioritized FCM-SMOTE: fuzzy memberships distinguish core vs. border samples, interpolation is prioritized in high-membership regions, and sampling weights are adjusted by membership, thereby significantly enhancing minority-class representation and suppressing noise diffusion while keeping the majority class fixed at a multiplier of 1.0. At the feature-learning level, we build three parallel branches across the spatial, temporal, and frequency domains: parallel Pyramid Depthwise Separable Convolutions (PDSC) to capture spatial (cross-feature) dependencies, BiGRU to model temporal dependencies, and wavelet transforms to extract multi-scale spectral statistics; their outputs are fused with learned weighting to form a complementary tri-domain representation. Experiments on the CICIDS2017 dataset show that the proposed method achieves state-of-the-art performance under both unsampled and sampled settings: Accuracy 99.39%, Precision 98.22%, Recall 93.69%, F1 95.56% in the comprehensive comparison; and Accuracy 98.97%, Recall 99.09%, F1 96.69% in a recall-oriented setting, markedly reducing missed detections while maintaining stable training/validation curves and a small generalization gap. These results show the effectiveness and reliability of clustering-guided oversampling combined with tri-domain multi-scale representation for IIoT intrusion detection.",
  "abstract_shown": true,
  "affiliations": [
    "College of Big Data and Information Engineering, Guizhou University, Guiyang, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuhan Yang"
    },
    {
      "name": "Damin Zhang"
    },
    {
      "name": "Qing He"
    },
    {
      "name": "Mu Lin"
    },
    {
      "name": "Ji Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d5t9ty70g8tqr.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0362087",
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  "id": "d5t9ty70g8tqr",
  "issns": [
    "1932-6203"
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  "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/Ecology/Industrial ecology"
    ],
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
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      "plos",
      "/Computer and information sciences/Computer networks/Internet"
    ],
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      "plos",
      "/Computer and information sciences/Computer networks/Internet/Internet of Things"
    ],
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      "/Computer and information sciences/Network analysis"
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      "/Ecology and environmental sciences/Ecology/Industrial ecology"
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      "plos",
      "/Engineering and technology/Industrial engineering/Industrial ecology"
    ],
    [
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      "/Physical sciences/Mathematics/Numerical analysis/Interpolation"
    ],
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      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Wavelet transforms"
    ]
  ],
  "openalex": "W7225420403",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/d5t9ty70g8tqr.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:24Z",
      "native_id": "10.1371/journal.pone.0362087",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0362087"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:ecology",
      "subfield": "ecology"
    },
    {
      "field": "mathematics",
      "origin": "plos:numerical analysis",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "Fuzzy-clustering–based multi-scale intrusion detection for the industrial internet of things",
  "title_text": "Fuzzy-clustering–based multi-scale intrusion detection for the industrial internet of things",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:48Z",
  "url": "https://research.draconitic.com/paper/d5t9ty70g8tqr",
  "venue": "PLOS One",
  "versions": []
}