JPEG Vigilant: AI-Powered Malware Image Detection

J. N. Ekatpure, Nilesh Kharade, Digvijay Korake, Dipak Kshirsagar and Rushikesh Mind

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 10, 66-70

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.

Keywords: Machine learning; malware; detection; JPEG; image; features. (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2361011
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