Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions

Authors

  • Shuguang Xiong

    1. Microsoft Inc., Beijing 100102, China
    2. Baidu Inc. Beijing 100085, China
  • Xiaoyang Chen

    The Ohio State University, Columbus, OH 43210, United States
  • Huitao Zhang

    Northen Arizona University, San Francisco St, AZ 86011, United States
  • Meng Wang

    Newmark Group, United States

DOI:

https://doi.org/10.30564/aia.v6i1.6718
Received: 25 March 2024 | Accepted: 26 April 2024 | Published Online: 29 June 2024

Abstract

This study addresses the challenge of Android malware detection, a critical issue due to the pervasive threats affecting mobile devices. As Android malware evolves, conventional detection methods struggle with novel or polymorphic malware that bypasses traditional defenses. This research leverages machine learning (ML) and deep learning (DL) techniques to overcome these limitations by adopting domain adaptation strategies that enhance model generalization across different distributions. The approach involves dividing a dataset into distinct distributions and applying domain adaptation techniques to ensure robustness and accuracy despite distribution shifts. Preliminary results demonstrate that domain adaptation significantly improves detection accuracy in target domains not represented in the training data. This paper showcases a domain adaptation-based method for Android malware detection, illustrating its potential to enhance security measures in dynamic environments. The findings suggest that integrating advanced ML and DL strategies with domain adaptation can substantially improve the efficacy of malware detection systems.

Keywords:

Component; Android malware detection; Deep learning; Domain adaptation

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How to Cite

Xiong, S., Chen, X., Zhang, H., & Wang, M. (2024). Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions. Artificial Intelligence Advances, 6(1), 13–24. https://doi.org/10.30564/aia.v6i1.6718

Issue

Vol. 6 , Iss. 1 (October 2024)

Article Type

Articles

License

Copyright © 2024 Shuguang Xiong, Xiaoyang Chen, Huitao Zhang, Meng Wang

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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