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Improving malware detection performance using hybrid deep representation learning with heuristic search algorithms.

Anuradha Anuradha1, Arun Singh Chouhan2, S Srinivas Rao3

  • 1Department of information technology, School of Engineering, Malla Reddy University, Hyderabad, Telangana, India. anuradha.anu503@gmail.com.

Scientific Reports
|January 8, 2026
PubMed
Summary

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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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This summary is machine-generated.

This study introduces a hybrid deep learning framework (IMDP-HDL) for advanced Android malware detection. The novel approach achieves 99.22% accuracy, outperforming existing methods against sophisticated threats.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Deep Learning

Background:

  • Android's market dominance necessitates robust malware protection due to sophisticated evasion tactics.
  • Traditional static analysis and machine learning struggle against advanced, dynamic malware.
  • Evasive techniques like obfuscation and dynamic code triggering challenge current detection methods.

Purpose of the Study:

  • To propose a hybrid deep learning framework (IMDP-HDL) for enhanced Android malware detection.
  • To ensure effective and scalable deployment of malware detection in real-world cybersecurity.
  • To address the limitations of conventional methods in identifying complex and evolving malware.

Main Methods:

  • Utilized Z-score standardization for consistent feature scaling and model performance.
Keywords:
AndroidCybercriminalsDeep leaningLong short-term memoryMalware detectionThreats

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  • Employed a hybrid deep learning model combining Convolutional Neural Network, Bi-directional Long Short-Term Memory, and Self-Attention mechanism (CBiLSTM-SA).
  • Conducted extensive experimentation on an Android malware dataset.

    • Main Results:

    • The IMDP-HDL model achieved a superior accuracy of 99.22%.
    • Demonstrated significant performance improvement over existing malware detection techniques.
    • Validated the framework's effectiveness on a comprehensive Android malware dataset.

    Conclusions:

    • The proposed IMDP-HDL framework offers a highly accurate and effective solution for Android malware detection.
    • The hybrid deep learning approach, CBiLSTM-SA, successfully mitigates advanced evasion tactics.
    • This methodology provides a scalable and reliable tool for real-world cybersecurity applications.