Classification of Malware Images Using Fine-Tunned ViT

Abstract

Malware detection and classification have become critical tasks in ensuring the security and integrity of computer systems and networks. Traditional methods of malware analysis often rely on signature-based approaches, which struggle to cope with the ever-evolving landscape of malware variants. In recent years, deep learning techniques have shown promising results in automating the process of malware classification. This paper presents a novel approach to malware image classification using the Vision Transformer (ViT) architecture. In this work, we adapt the ViT model to the domain of malware analysis by representing malware images as input tokens to the ViT architecture. To evaluate the effectiveness of the proposed approach, we used a comprehensive dataset comprising 14,226 malware samples across 26 families. We compare the performance of our ViT-based classifier with traditional machine learning methods and other deep learning architectures. Our experimental results showcase the potential of the ViT in handling malware images, achieving a classification accuracy of 98.80%. The presented approach establishes a strong foundation for further research in utilizing state-of-the-art deep learning architectures for enhanced malware analysis and detection techniques.