2021/11/03 by Erik R. Larsen, Larsen, Erik, Korey MacVittie +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Cell Image Analysis Techniques #Digital Media Forensic Detection #FOS: Computer and information sciences #Image Processing Techniques and Applications #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2111.02375
openalex publication_date 2021/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.