Image-based Malware Detection and Classification Approach Using Multi-level Deep Learning Methods

Malware poses a significant threat to cybersecurity, continuously evolving to evade traditional detection methods.Deep learning (DL) offers a promising avenue for addressing this challenge by leveraging its ability to learn complex patterns from large amounts of data.This paper introduces a novel deep learning architecture that combines convolutional neural network (CNN), long short-term memory network (LSTM), and radial basis function network (RBF) to extract discriminative features from malware images.Our model captures both low-level visual details and high-level semantic information for robust and accurate malware classification.The Softmax layer assigns probabilities to different malware classes, enabling precise classification.We evaluated the model on three diverse datasets: Malimg, MaleVis, and Microsoft Big 2015, each differing in malware families, image characteristics, and collection methods.The model achieved accuracy rates of 99.06%, 93.55%, and 97.52% on these datasets, respectively, demonstrating its effectiveness and potential in malware image classification.