{
  "abstract": "The rapid expansion of digital systems has intensified the complexity of cyber threats, rendering traditional intrusion detection systems (IDS) inadequate against evolving attacks. This study proposes a hybrid IDS (H-IDS) that integrates supervised (SVM, Random Forest, CatBoost, DNN) and unsupervised (Isolation Forest, One-Class SVM, Autoencoder) models within an ensemble framework. Preprocessing employs PCA for dimensionality reduction and SMOTE for class balancing, while weighted voting based on cross-validation F1-scores optimizes ensemble decisions. A lightweight blockchain-inspired hash-chained audit log provides tamper-evident logging of detection events in a single-node deployment without decentralized consensus. Evaluated on NSL-KDD and CIC-IDS2017 datasets, H-IDS achieves 98.85% accuracy (pre-blockchain) and 98.15% (post-blockchain). The ledger operates in a private, single-node setting and introduces minimal local logging overhead and observed reductions in false positives (paired t-test, n = 3, p < 0.05). This work advances trustworthy, auditable, and high-performance intrusion detection for modern network environments.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al Majmaah, Saudi Arabia",
    "Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majmaah, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shailendra Mishra"
    },
    {
      "affs": [
        1
      ],
      "name": "Reem Alshenaifi"
    },
    {
      "name": "Ruba Ahmed Alfahidah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2xb2fp9kxpnr7.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0356878",
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  "id": "2xb2fp9kxpnr7",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0356878",
  "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",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms"
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      "plos",
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      "plos",
      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
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      "/Medicine and health sciences/Diagnostic medicine/Clinical laboratory sciences/Forensics"
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    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Machine learning algorithms"
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      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms"
    ]
  ],
  "openalex": "W7212255497",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356878&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/2xb2fp9kxpnr7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0356878",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0356878"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    }
  ],
  "title": "A lightweight blockchain-inspired hybrid intrusion detection system with ensemble learning for tamper-proof auditing",
  "title_text": "A lightweight blockchain-inspired hybrid intrusion detection system with ensemble learning for tamper-proof auditing",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/2xb2fp9kxpnr7",
  "venue": "PLOS One",
  "versions": []
}