MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION - A META-ANALYSIS LITERATURE REVIEW

Aug 2, 2026

Upload by Liam

With a text mining and bibliometrics approach, this study reviews literature on the evolution of malware classification using machine learning, covering research from 2008 to 2022. The work targets three research questions: whether machine learning applications for malware classification are growing, which application type is most common, and what outcomes follow from the most prevalent applications. An analysis of 2186 peer-reviewed articles uses quantitative and qualitative methods including statistical and N-gram analysis plus formal literature review.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

Abstract

Keywords

1. Introduction

Dakota State University Beadle Scholar | Research & Publications | Beacom College of Computer and Cyber Sciences | | --- | --- | | 2-2023 MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITERATURE REVIEW T.J. Nelson Austin O'Brien Dakota State University Cherie Noteboom Dakota State University Follow this and additional works at: [https://scholar.dsu.edu/ccspapers](https://scholar.dsu.edu/ccspapers) Part of the Computer and Systems Architecture Commons | | Recommended Citation Nelson, T., O’Brien, A., & Noteboom, C. (2023) . Machine Learning Applications in Malware Classification: A Meta-Analysis Literature Review. International Journal on Cybernetics & Informatics (IJCI), 12(12), 113. This Article is brought to you for free and open access by the Beacom College of Computer and Cyber Sciences at Beadle Scholar. It has been accepted for inclusion in Research & Publications by an authorized administrator of Beadle Scholar. For more information, please contact [repository@dsu.edu](repository@dsu.edu). MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A META ANALYSIS LITERATURE REVIEW Tjada Nelson, Austin O’Brien and Cherie Noteboom Beacom College of Computer & Cyber Sciences, Dakota State University, Madison, South Dakota ABSTRACT With a text mining and bibliometrics approach, this study reviews the literature on the evolution of malware classification using machine learning. This work takes literature from 2008 to 2022 on the subject of using machine learning for malware classification to understand the impact of this technology on malware classification. Throughout this study, we seek to answer three main research questions: RQ1: Is the application of machine learning for malware classification growing? RQ2: What is the most common machine-learning application for malware classification? RQ3: What are the outcomes of the most common machine learning applications? The analysis of 2186 articles resulting from a data collection process from peerreviewed databases shows the trajectory of the application of this technology on malware classification as well as trends in both the machine learning and malware classification fields of study. This study performs quantitative and qualitative analysis using statistical and N-gram analysis techniques and a formal literature review to answer the proposed research questions. The research reveals methods such as support vector machines and random forests to be standard machine learning methods for malware classification in efforts to detect maliciousness or categorize malware by family. Machine learning is a highly researched technology with many applications, from malware classification and beyond. KEYWORDS Malware, Malware Classification, Machine Learning. 1. INTRODUCTION Machine learning is a technology that has been at the forefront of academic research since its inception in the 1990s [1] . Applications from Machine learning is defined as the capacity for a system to learn from a problem-specific data source to identify patterns in that data build that provide insight around that data [2] . The technology behind machine learning enables a wide range of efficiency for computer systems across many disciplines. Therefore, machine learning has been the subject of many papers in academia. As of December 2022, Google Scholar, an academic article search engine and aggregator, returns over 5.4 million results for the term“machine learning.” Through this research, machine learning has successfully been applied to several real-world applications ranging from healthcare to gaming. Machine learning can be divided into three types: supervised, unsupervised, and reinforcement. Supervised machine learning takes test data representing known desired results and is used to produce a system that predicts future results. For example, supervised machine learning is the method to perform classification or face reco

FAQs

What is the scope of the literature reviewed in this study?

The study reviews literature from 2008 to 2022 focused on applying machine learning to malware classification.

What research questions does the study aim to answer?

It examines whether applications are growing (RQ1), which application is most common (RQ2), and the outcomes of the most common applications (RQ3).

Which machine learning methods are reported as standard for malware classification?

Support vector machines and random forests are described as common methods used to detect maliciousness or categorize malware by family.

Aug 2, 2026

Upload by Liam