# An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

**Authors:** Pratikkumar Prajapati, Mark Stamp  
**arXiv:** [2103.13827](https://arxiv.org/abs/2103.13827)  
**Date:** 2021-03-24  
**Summary page:** https://www.summarizepaper.com/fr/paper/2103.13827v1/

## 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.