A Novel Approach to Malware Detection using Machine Learning and Image Processing
Saadaldeen Rashid Ahmed, Salah J. Mohamed, Mohammed S. Aljanabi, Sameer Algburi, Duaa A. Majeed, Neesrin Ali Kurdi, Mohammed Al-Sarem, Jamal Fadhil Tawfeq
Abstract
Studies have emphasized the limitations of existing methods in effectively detecting advanced strains of malware. To address this gap, this research presents a novel approach that combines machine learning and image processing techniques for malware identification. Specifically, convolutional neural networks (CNN), decision trees (DT), and random forests (RF) algorithms are utilized to analyze grayscale images representing malware files. The proposed hybrid model is evaluated using the Malimg dataset, which contains diverse samples from multiple malware families. The experimental results demonstrate high accuracy rates, with CNN achieving 93%, CNN-DT achieving 96%, and CNN-RF achieving 94.58%. This study makes a significant contribution to the advancement of malware detection technologies by exploring the potential of image-based features. It highlights the possibility of leveraging such features to enhance cybersecurity protection against emerging threats.