STEGO Malware Detection System

Authors: Kakad Gauri Dilip, Junagade Sanika Bipinchandra, More Gauri Rajendra, Ahire Kunal Himmat, Asst. Prof. V. D. Mane

Country: India

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Abstract: In the modern digital world, images are widely shared across online platforms, making them a potential medium for hidden cyber threats such as stegomalware. These threats embed malicious code within image files like GIF, PNG, and JPEG, often bypassing traditional security systems. This paper presents a stego malware detection system that uses a Convolutional Neural Network (CNN) to analyze images and identify hidden malware. The system includes image preprocessing and provides a web-based interface where users can upload images and receive real-time results. If a threat is detected, the system alerts the user and provides safety recommendations. The proposed solution ensures accurate detection, fast processing, and improved security against image-based cyberattacks.

Keywords: Detection, CNN, Image Malware, Cybersecurity, Deep Learning, Image Analysis, Web-Based System

Paper Id: 233088

Published On: 2026-04-30

Published In: Volume 14, Issue 2, March-April 2026

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