IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v9y2023i10idhcseit2361011.html

JPEG Vigilant : AI-Powered Malware Image Detection

Author

Listed:
  • J. N. Ekatpure
  • Nilesh Kharade
  • Digvijay Korake
  • Dipak Kshirsagar
  • Rushikesh Mind

Abstract

Cyberattacks against people, companies, and organizations have risen in recent years.In order to conduct an attack, cybercriminals are constantly searching for efficient channels to spread malware to targets. Millions of people use photos every day, and the majority of consumers believe that they are safe to use. However, some types of images may contain malicious payloads that carry out dangerous functions. Due in large part to its lossy compression, JPEG is the most widely used image for mat.In this study, we introduce JPEGVigilant, the first machine learning-based method designed exclusively for the quick and accurate identification of unknown malicious JPEG images. In order to distinguish between benign and malicious JPEG images, JPEGVigilant statically derives 10 straightforward yet discriminative properties from the JPEG LE structure.

Suggested Citation

  • J. N. Ekatpure & Nilesh Kharade & Digvijay Korake & Dipak Kshirsagar & Rushikesh Mind, 2023. "JPEG Vigilant : AI-Powered Malware Image Detection," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 9(10), pages 66-70, October.
    • Handle: RePEc:jbh:ijsrcs:v9:y2023:i10:id:hcseit2361011
    Note: Article URL: https://ijsrcseit.com/CSEIT2361011
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT2361011
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT2361011.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    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:jbh:ijsrcs:v9:y2023:i10:id:hcseit2361011. 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 (USA) (email available below). General contact details of provider: https://ijsrcseit.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.