Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Robust Android malware detection through EfficientNet–BERT fusion and Jaya optimization for behavioral analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

查看全文
  • Faisal S. Alsubaei,
  • Abdulwahab Ali Almazroi,
  • Walid Said Atwa,
  • Abdulaleem Ali Almazroi,
  • Nasir Ayub,
  • Noor Zaman Jhanjhi

摘要

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.