IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v16y2025i2d10.1007_s13198-024-02643-x.html
   My bibliography  Save this article

A brief survey of deep learning methods for android Malware detection

Author

Listed:
  • Abdurraheem Joomye

    (Sunway University)

  • Mee Hong Ling

    (Sunway University)

  • Kok-Lim Alvin Yau

    (Universiti Tunku Abdul Rahman (UTAR))

Abstract

As the number of malware attacks continues to grow year by year with increasing complexity, Android devices have remained vulnerable with over 30 million mobile attacks detected in 2023. Thus, it has become more challenging to detect recent malware using traditional methods, such as signature-based and heuristic-based methods. Meanwhile, there has been a rise in the application and research of machine learning (ML) and deep learning (DL). As a result, researchers have proposed ML- and DL-based methods for Android malware detection. This paper reviews the methods proposed in the literature for Android malware detection using DL. It establishes a taxonomy highlighting and explores the feature types extracted through static and dynamic analyses and the DL models used in the literature. It also illustrates which feature types have been used with the different DL models. Finally, it discusses major challenges and potential future directions in the field of ML and DL methods for Android malware detection such as the need for updated datasets, more on-device evaluation of the methods and more approaches using dynamic/hybrid analyses.

Suggested Citation

  • Abdurraheem Joomye & Mee Hong Ling & Kok-Lim Alvin Yau, 2025. "A brief survey of deep learning methods for android Malware detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(2), pages 711-733, February.
  • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02643-x
    DOI: 10.1007/s13198-024-02643-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-024-02643-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-024-02643-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02643-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.