{
  "abstract": "The increasing sophistication of cyber-attacks necessitates intrusion detection systems (IDS) that are both accurate and computationally efficient, particularly in high-dimensional and class-imbalanced environments. Although ensemble-based IDS models enhance predictive performance, most existing approaches treat feature selection and ensemble aggregation as independent stages, limiting integrated optimization between feature representation and decision-making. To overcome these limitations, this paper proposes Adaptive Feature Intelligence for Intrusion Detection (AFI-ID), an intrusion detection framework that integrates hybrid adaptive feature intelligence with attention-based deep stacking. The proposed AFI module combines statistical relevance measures—Mutual Information and Chi-square with model-based importance derived from XGBoost and Extra Trees through a weighted fusion strategy. This strategy enables the selection of a compact and discriminative feature subset while preserving sensitivity to minority attack classes. At the ensemble level, heterogeneous base learners generate structured meta-features, including log-probability representations, prediction confidence, and entropy. These meta-features are processed by an attention-based Bidirectional Long Short-Term Memory (BiLSTM) meta-classifier, which dynamically weights classifier contributions to achieve context-aware decision fusion. Experimental evaluation on the UNSW-NB15 and CICIDS2017 datasets under binary and multiclass settings demonstrates that AFI-ID achieves strong performance relative to the evaluated baseline classifiers under the experimental protocols considered in this study. AFI-ID achieves 93.31% accuracy with a Receiver Operating Characteristic—Area Under Curve (ROC-AUC) of 0.9849 in binary classification and 79.95% accuracy with a ROC-AUC of 0.9845 in multiclass classification on UNSW-NB15. On CICIDS2017, AFI-ID achieves 98.33% accuracy with a ROC-AUC of 0.9976 in binary classification and 93.54% accuracy with a ROC-AUC of 0.999 in multiclass classification. In repeated UNSW-NB15 binary experiments across five random seeds, AFI-ID achieved a mean accuracy of 93.62%, with paired t-tests indicating statistically significant improvements over Logistic Regression (p = 1.08 × 10−10), Extra Trees (p = 1.38 × 10−5), and XGBoost (p = 3.69 × 10−5). These results demonstrate the effectiveness of the proposed adaptive feature selection and attention-based decision fusion strategy under high-dimensional and class-imbalanced network traffic conditions.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Engineering, Vellore Institute of Technology , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Antony Joseph Raj"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Madiajagan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e3w4b55jexj2m.bib",
  "cited_by": 0,
  "doi": "10.3389/frai.2026.1950536",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13724397/?tool=EBI",
  "id": "e3w4b55jexj2m",
  "issns": [
    "2624-8212"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13724397",
  "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",
      "Original Research"
    ],
    [
      "openalex",
      "1705|Network Security and Intrusion Detection"
    ]
  ],
  "openalex": "W7222914235",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13724397/pdf/?tool=EBI",
  "pmcid": "PMC13724397",
  "pmid": "42973665",
  "published": "2026-10-19",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/e3w4b55jexj2m.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T15:20:51Z",
      "native_id": "PMC:PMC13724397",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13724397"
    },
    {
      "first_seen": "2026-11-04T12:22:58Z",
      "native_id": "PMC13724397",
      "source": "pmc",
      "url": "https://doi.org/10.3389/frai.2026.1950536"
    },
    {
      "first_seen": "2026-11-04T15:20:48Z",
      "native_id": "MED:42973665",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42973665"
    }
  ],
  "subjects": [],
  "title": "AFI-ID: adaptive feature intelligence for intrusion detection",
  "title_text": "AFI-ID: adaptive feature intelligence for intrusion detection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:20:53Z",
  "url": "https://research.draconitic.com/paper/e3w4b55jexj2m",
  "venue": "Frontiers in artificial intelligence",
  "versions": []
}