An Empirical Analysis of Image-Based Learning Techniques for Malware Classification
Abstract
In this paper, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Amongst our CNN experiments, transfer learning plays a prominent role specifically, we test the VGG-19 and ResNet152 models. As compared to previous work, the results presented in this paper are based on a larger and more diverse malware dataset, we consider a wider array of features, and we experiment with a much greater variety of learning techniques. Consequently, our results are the most comprehensive and complete that have yet been published.
Cite
@article{arxiv.2103.13827,
title = {An Empirical Analysis of Image-Based Learning Techniques for Malware Classification},
author = {Pratikkumar Prajapati and Mark Stamp},
journal= {arXiv preprint arXiv:2103.13827},
year = {2021}
}
Comments
20 pages, 8 figures, 7 tables