Virus-MNIST: Machine Learning Baseline Calculations for Image Classification
This provides a new benchmark for malware classification, but it is incremental as it adapts an existing dataset style to a specific domain.
The authors introduced Virus-MNIST, a dataset of malware images for benchmarking virus classifiers, and found that LightGBM, Gradient Boosting, and Random Forest achieved the highest accuracy scores.
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.