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Graph-augmented multi-modal learning framework for robust android malware detection.

Muhammad Usama Tanveer1, Kashif Munir1, Hasan J Alyamani2,3

  • 1Institute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, RahimYar Khan, 64200, Pakistan.

Scientific Reports
|November 3, 2025
PubMed
Summary

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This summary is machine-generated.

GIT-GuardNet, a novel Graph-Informed Transformer Network, effectively detects Android malware by fusing static code, call graphs, and temporal behavior. This advanced system achieves high accuracy and robustness against sophisticated threats.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Android malware poses a significant mobile security challenge due to sophisticated evasion techniques.
  • Traditional detection methods struggle with obfuscation and lack multi-domain contextual integration.

Purpose of the Study:

  • To introduce GIT-GuardNet, a novel Graph-Informed Transformer Network for precise and robust Android malware detection.
  • To leverage multi-modal learning by integrating static code, call graph structures, and temporal behavior.

Main Methods:

  • Developed GIT-GuardNet, a network fusing Transformer encoder (static code), Graph Attention Network (call graphs), and Temporal Transformer (behavior).
  • Employed a cross-attention fusion mechanism to dynamically weigh inter-modal dependencies for informed decision-making.
Keywords:
Android malware detectionCall graph modellingCross-attention fusionDeep learningMulti-modal learningStatic code analysisTemporal behavior analysis

Related Experiment Videos

  • Conducted experiments on a dataset of 15,036 Android applications, including 5,560 malware samples.

    • Main Results:

    • Achieved state-of-the-art performance with 99.85% accuracy, 99.89% precision, and 99.94% AUC.
    • Outperformed traditional machine learning, single-view deep networks, and hybrid approaches like DroidFusion.
    • Demonstrated strong generalization against obfuscated and stealthy threats with low inference overhead.

    Conclusions:

    • GIT-GuardNet offers a powerful and extensible framework for intelligent Android malware defense.
    • The multi-modal approach and cross-attention fusion significantly enhance detection capabilities.
    • The system shows practical applicability for real-world mobile threat detection.