Malware Classification using Image Analysis

Abstract

Malware detection and classification have evolved significantly with the integration of pattern recognition and image classification techniques. A pioneering study by Nataraj et al. (2011) [1] demonstrated that malware binaries could be visualized as grayscale images, revealing structural and textural similarities among malware families. Inspired by this approach, this research explores the effectiveness of deep learning-based architectures, specifically the hybrid CoatNet model, in improving malware classification accuracy. Using the MalImg dataset, we investigate the performance of CoatNet in identifying and categorizing various malware families, comparing its results with traditional image-processing-based classification methods. Our 
findings indicate that deep learning techniques offer superior accuracy and robustness in detecting malicious software without requiring code disassembly or execution. As malware threats continue to proliferate, advanced AI-driven approaches provide a scalable and efficient solution for real-time malware detection 
and cybersecurity enhancement.