IDEAS home Printed from https://ideas.repec.org/a/etm/ijsrst/v12y2025i3id812.html

PDF Malware Detection: Toward Machine Learning Modelling with Explainability Analysis

Author

Listed:
  • K Naresh
  • Thukivakam Dharani

Abstract

In the digital age, PDF files are widely used for document sharing, but their popularity also makes them a target for malware attacks. This project, titled " Detecting Malware in PDFs: Advancing Machine Learning Models with Interpretability Assessment," aims the goal is to design and assess machine learning models aimed at identifying malware within PDF files. Utilizing a dataset from Kaggle, which contains labelled examples of malicious and benign PDFs, various algorithms including RF, C5.0, J48, SVM, AdaBoost, DNN, GBM, and KNN will be applied. The primary focus is on achieving high detection accuracy while also providing explainability to gain insight into how the models make decisions. By leveraging machine learning techniques, this project seeks to enhance cybersecurity measures, offering a robust solution to identify and mitigate potential threats embedded in PDF documents.

Suggested Citation

  • K Naresh & Thukivakam Dharani, 2025. "PDF Malware Detection: Toward Machine Learning Modelling with Explainability Analysis," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 12(3), pages 170-180, June.
    • Handle: RePEc:etm:ijsrst:v12:y2025:i3:id:812
    DOI: 10.32628/IJSRST2512322
    as

    Download full text from publisher

    File URL: https://ijsrst.com/home/article/view/IJSRST2512322
    File Function: Abstract page
    Download Restriction: no
    File URL: https://ijsrst.com/home/article/download/IJSRST2512322/IJSRST2512322
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/IJSRST2512322?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

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:etm:ijsrst:v12:y2025:i3:id:812. 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: Pankaj Sharma (email available below). General contact details of provider: https://ijsrst.com/home .

    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.