Harnessing image-based deep learning for advanced malware classification

Abstract

This thesis explores the application of image-based deep learning models for malware classification, leveraging a subset of the extensive MalNet-Image dataset, which includes around 87,000 binary images from a base of 1.2 million binary images based on Android APK files. The core contribution of this work lies in the innovative use of multiple components that, as far as we know, have not been used before to tackle the malware classification problem. Harnessing the power of deep neural networks (DNNs), which have demonstrated exceptional capabilities in various classification tasks, we aim to enhance the accuracy and efficiency of malware detection. These include Feature Pyramid Networks (FPN) to handle the file size scale issue when converting to images and the application of data augmentation techniques like MIXUP and TrivialAugment. We employ transfer learning with pre-trained models on ImageNet and optimize them using the AdamW Schedule-Free optimizer. Our experimental results show that the integration of these techniques achieves remarkable improvement in classification accuracy, with our best model achieving an F1 score of 0.6927 compared to 0.65 reported on the provided split for MalNet-Tiny. This could be considered a step forward in the field of malware classification using image-based deep learning models.Graduate2025-08-2