# Virus-MNIST: Machine Learning Baseline Calculations for Image Classification

arXiv: https://arxiv.org/abs/2111.02375 · HTML: https://mlsanity.com/paper/2111.02375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [A Convolutional Transformation Network for Malware Classification](https://mlsanity.com/paper/1909.07227/) (2019)
- [Virus-MNIST: A Benchmark Malware Dataset](https://mlsanity.com/paper/2103.00602/) (2021)

## Abstract (arXiv)

The Virus-MNIST data set is a collection of thumbnail images that is similar in style to the ubiquitous MNIST hand-written digits. These, however, are cast by reshaping possible malware code into an image array. Naturally, it is poised to take on a role in benchmarking progress of virus classifier model training. Ten types are present: nine classified as malware and one benign. Cursory examination reveals unequal class populations and other key aspects that must be considered when selecting classification and pre-processing methods. Exploratory analyses show possible identifiable characteristics from aggregate metrics (e.g., the pixel median values), and ways to reduce the number of features by identifying strong correlations. A model comparison shows that Light Gradient Boosting Machine, Gradient Boosting Classifier, and Random Forest algorithms produced the highest accuracy scores, thus showing promise for deeper scrutiny.