{
  "abstract": "The rapid growth of the Internet has made network attacks increasingly frequent, stealthy, and diverse, posing stringent requirements on the accuracy and latency of network intrusion detection systems (NIDS). Although deep-learning-based NIDS can achieve strong detection performance, their large parameter size and high computational cost hinder deployment on resource-constrained edge devices. To address this limitation, we propose a temperature-adaptive logit-standardized knowledge distillation framework for lightweight network intrusion detection. A high-capacity teacher network first learns discriminative representations from network traffic, and a compact student network is then trained to mimic the teacher under a tailored distillation scheme. During distillation, we apply per-sample Z-score standardization to the logits of both teacher and student models to mitigate scale mismatch and emphasize relative inter-class relationships. Moreover, we adapt the distillation temperature according to sample difficulty and training progress, enabling suitably smoothed soft targets across samples and stages, and jointly optimize the student with hard-label supervision. Experiments on benchmark datasets including NSL-KDD and CIC-IDS2017 demonstrate that the proposed method substantially reduces model complexity while maintaining stable and competitive detection performance. Additional repeated-run, ablation, and lightweight baseline analyses further indicate that the proposed framework achieves a favorable accuracy–complexity trade-off.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer, Xijing University, Xi'an, Shaanxi, China.",
    "Xi'an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, School of Computer Science, Xijing University, Xi'an, Shaanxi, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "XiaoFang Dong",
      "orcid": "0009-0007-0017-5897"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Kai Yang",
      "orcid": "0000-0002-7338-3765"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "JingChao Liu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Wen Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1xn04x1595w3q.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0361860",
  "fulltext_url": "https://europepmc.org/articles/PMC13726990",
  "id": "1xn04x1595w3q",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0361860",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Adaptive Training"
    ],
    [
      "heading",
      "Artificial Intelligence"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Cognitive Psychology"
    ],
    [
      "heading",
      "Cognitive Science"
    ],
    [
      "heading",
      "Computer and Information Sciences"
    ],
    [
      "heading",
      "Deep Learning"
    ],
    [
      "heading",
      "Distillation"
    ],
    [
      "heading",
      "Education"
    ],
    [
      "heading",
      "Entropy"
    ],
    [
      "heading",
      "Human Learning"
    ],
    [
      "heading",
      "Learning"
    ],
    [
      "heading",
      "Learning and Memory"
    ],
    [
      "heading",
      "Machine Learning"
    ],
    [
      "heading",
      "Neural Networks"
    ],
    [
      "heading",
      "Neuroscience"
    ],
    [
      "heading",
      "People and Places"
    ],
    [
      "heading",
      "Physical Sciences"
    ],
    [
      "heading",
      "Physics"
    ],
    [
      "heading",
      "Population Groupings"
    ],
    [
      "heading",
      "Professions"
    ],
    [
      "heading",
      "Psychology"
    ],
    [
      "heading",
      "Recurrent Neural Networks"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "heading",
      "Research and Analysis Methods"
    ],
    [
      "heading",
      "Separation Processes"
    ],
    [
      "heading",
      "Social Sciences"
    ],
    [
      "heading",
      "Sociology"
    ],
    [
      "heading",
      "Teachers"
    ],
    [
      "heading",
      "Thermodynamics"
    ],
    [
      "heading",
      "Training (Education)"
    ],
    [
      "openalex",
      "1705|Network Security and Intrusion Detection"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Learning and memory/Learning/Human learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neural networks/Recurrent neural networks"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Neural networks/Recurrent neural networks"
    ],
    [
      "plos",
      "/People and places/Population groupings/Professions/Teachers"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Entropy"
    ],
    [
      "plos",
      "/Research and analysis methods/Separation processes/Distillation"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Education/Training (education)/Adaptive training"
    ]
  ],
  "openalex": "W7225109628",
  "pdf_url": "https://europepmc.org/articles/PMC13726990?pdf=render",
  "pmcid": "PMC13726990",
  "pmid": "42970586",
  "published": "2026-11-02",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1xn04x1595w3q.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:16Z",
      "native_id": "10.1371/journal.pone.0361860",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0361860"
    },
    {
      "first_seen": "2026-11-04T15:20:43Z",
      "native_id": "MED:42970586",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42970586"
    },
    {
      "first_seen": "2026-11-05T12:23:12Z",
      "native_id": "PMC13726990",
      "source": "pmc",
      "url": "https://doi.org/10.1371/journal.pone.0361860"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "physics",
      "origin": "heading:Thermodynamics",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "A temperature-adaptive logit-standardized knowledge distillation for lightweight network intrusion detection",
  "title_text": "A temperature-adaptive logit-standardized knowledge distillation for lightweight network intrusion detection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:22:48Z",
  "url": "https://research.draconitic.com/paper/1xn04x1595w3q",
  "venue": "PLOS One",
  "versions": []
}