MalwareVison: A Deep Learning-Driven Approach For Malware Classification

Abstract

The fast propagation of malware across the internet requires the development of advanced classification and detection techniques. Traditional signature-based detection malware methods often fail to identify new and obfuscated variants which demand advanced machine learning-based solutions. We propose MalwareVision, a framework based on deep learning for the classification of malware samples. The model was trained on the Malimg dataset comprising images of 9,339 malware images across 25 families and evaluated based on accuracy, precision, recall, and F1-score metrics. We observe that the model achieved an impressive accuracy of 95.09% in both the training and testing datasets and that the precision and recall values remained high for most malware families. The results highlight the effectiveness of deep learning-based Convolutional Neural Network (CNN) for malware classification. The proposed MalwareVision framework offers a scalable, automated solution for malware classification, contributing to the advancement of AI-driven cybersecurity defenses.