Detecting malicious code variants using convolutional neural network (CNN) with transfer learning

Abstract

Malware presents a significant threat to computer networks and devices that lack robust defense mechanisms, despite the widespread use of anti-malware solutions. The rapid growth of the Internet has led to an increase in malicious code attacks, making them one of the most critical challenges in network security. Accurate identification and classification of malware variants are crucial for preventing data theft, security breaches, and other cyber risks. However, existing malware detection methods are often inefficient or inaccurate. Prior research has explored converting malicious code into grayscale images, but these approaches are often computationally intensive, especially in binary form. To address these challenges, we propose the Malware Variants Detection System (MVDS), a novel technique that transforms malicious code into color images, enhancing malware detection capabilities compared to traditional methods. Our approach leverages the richer information in color images to achieve higher classification accuracy than grayscale-based methods. We further improve the detection process by employing transfer learning to automatically identify and classify malware images based on their distinctive features. Empirical results demonstrate that MVDS achieves 97.98% accuracy with high detection speed, highlighting its potential for practical implementation in strengthening network security.