Virus-MNIST: Machine Learning Baseline Calculations for Image Classification

• URL: http://arxiv.org/abs/2111.02375v1
• Date: Wed, 3 Nov 2021 17:44:23 GMT
• Title: Virus-MNIST: Machine Learning Baseline Calculations for Image Classification
• Authors: Erik Larsen, Korey MacVittie, and John Lilly
• Abstract summary: The Virus-MNIST data set is a collection of thumbnail images that is similar in style to the ubiquitous MNIST hand-written digits. It is poised to take on a role in benchmarking progress of virus model training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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