Malware Classification Using a Hybrid CNN-Autoencoder and Transformer Encoded Approach

Malware remains a persistent and growing threat in the digital space, making it essential for the development of accurate and efficient detection techniques.This study proposes a hybrid deep learning model that combines Convolutional Neural Networks (CNNs), Autoencoders (AEs), and Vision Transformers (ViTs) for malware classification using Portable Executable (PE) header metadata, evaluated using the ClaMP_Integrated-5184 dataset.In the Proposed Architecture, the CNN component extracts local spatial features, the Autoencoder compresses and denoises the feature space, and the Vision Transformer captures global dependencies for robust classification.The results show that the model achieved a classification accuracy of 98% and an F1score of 98%, outperforming benchmark and state-of-the-art models.Findings highlight the effectiveness of hybrid deep learning architectures in static malware detection and demonstrate a promising approach for enhancing real-time malware detection systems.