{
  "abstract": "Android malware continues to evolve through obfuscation, permission misuse, and dynamic payload activation, reducing the effectiveness of conventional detection methods. Existing machine learning and deep learning approaches often face challenges related to class imbalance, redundant high-dimensional features, and limited robustness to emerging malware behaviors. To address these limitations, this study proposes ENBT-J, a hybrid ensemble framework for Android malware detection. The proposed pipeline integrates Edited Nearest Neighbors (ENN) for noise filtering and class balancing, Boruta-based feature selection for identifying informative attributes, MobileNetV2 for compact feature embedding, and an EfficientNet–BERT classification module for joint modeling of static and dynamic behavioral characteristics. Jaya-based hyperparameter tuning is further employed to improve training stability. Experimental evaluation on multiple Android malware datasets shows that ENBT-J achieves 98.14% accuracy, 97.70% F1-score, 98.0% PR-AUC, and 96.15% MCC, outperforming several recent baseline methods. These findings suggest that ENBT-J provides a robust and scalable framework for Android malware detection under diverse and noisy data conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah",
    "College of Computing and Information Technology at Khulais, Department of Information Technology, University of Jeddah",
    "Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University",
    "Department of Creative Technologies, Faculty of Computing and AI, Air University Islamabad",
    "School of Computing and Artificial Intelligence, aculty of Engineering and Technology, Sunway University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Faisal S. Alsubaei"
    },
    {
      "affs": [
        1
      ],
      "name": "Abdulwahab Ali Almazroi"
    },
    {
      "affs": [
        1
      ],
      "name": "Walid Said Atwa"
    },
    {
      "affs": [
        2
      ],
      "name": "Abdulaleem Ali Almazroi"
    },
    {
      "affs": [
        3
      ],
      "name": "Nasir Ayub"
    },
    {
      "affs": [
        4
      ],
      "name": "Noor Zaman Jhanjhi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fpwxewc3zcjxx.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-63065-2",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-63065-2",
  "id": "fpwxewc3zcjxx",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-63065-2",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1711|Advanced Malware Detection Techniques"
    ]
  ],
  "openalex": "W7171724700",
  "pdf_url": "https://www.nature.com/articles/s41598-026-63065-2_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/fpwxewc3zcjxx.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T14:21:19Z",
      "native_id": "06669920e9674a35bb7500edd7ec41a0",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-63065-2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Robust Android malware detection through EfficientNet–BERT fusion and Jaya optimization for behavioral analysis",
  "title_text": "Robust Android malware detection through EfficientNet–BERT fusion and Jaya optimization for behavioral analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T14:20:45Z",
  "url": "https://research.draconitic.com/paper/fpwxewc3zcjxx",
  "venue": "Scientific Reports",
  "versions": []
}