Malware remains a major cybersecurity concern, which demands effective techniques for accurate detection and classification. This study presents a novel framework that leverages binary image representations of malware to enhance classification performance. The process begins by transforming malware files from their hexadecimal form into binary data, which is then converted to grayscale images serving as input for deep learning models. The study also examines the distinctive visual characteristics of various malware families, revealing how structural patterns in binary images are correlated with classification outcomes. By examining the role of image processing and deep learning, the research provides valuable insight into the intersection of artificial intelligence and cybersecurity. The findings highlight the strength of CNNs for malware classification, while acknowledging the complementary potential of ResNet and Autoencoder-based approaches. As cyber threats become increasingly sophisticated, advancing detection methods is essential. This work demonstrates that combining deep learning with binary image analysis presents a promising approach to developing more resilient malware detection systems and enhanced protection for digital environments. Three architectures—Convolutional Neural Networks (CNN), Residual Networks (ResNet), and Autoencoders—are systematically evaluated using a dataset of 3,240 malware samples categorized into nine families. The dataset is carefully divided into training and testing sets, and all images are resized to maintain consistency between inputs. Among the evaluated models, CNN with image-scaling techniques shows a superior accuracy of 91%, outperforming the ResNet and Autoencoder models, which achieve accuracies of 86% and 85%, respectively.
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