IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0296722.html
   My bibliography  Save this article

AMDDLmodel: Android smartphones malware detection using deep learning model

Author

Listed:
  • Muhammad Aamir
  • Muhammad Waseem Iqbal
  • Mariam Nosheen
  • M Usman Ashraf
  • Ahmad Shaf
  • Khalid Ali Almarhabi
  • Ahmed Mohammed Alghamdi
  • Adel A Bahaddad

Abstract

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications’ endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user’s privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

Suggested Citation

  • Muhammad Aamir & Muhammad Waseem Iqbal & Mariam Nosheen & M Usman Ashraf & Ahmad Shaf & Khalid Ali Almarhabi & Ahmed Mohammed Alghamdi & Adel A Bahaddad, 2024. "AMDDLmodel: Android smartphones malware detection using deep learning model," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-16, January.
  • Handle: RePEc:plo:pone00:0296722
    DOI: 10.1371/journal.pone.0296722
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296722
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0296722&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0296722?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0296722. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.