[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117278-en":3,"doc-seo-117278-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117278,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION - A META-ANALYSIS LITERATURE REVIEW","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.","Dakota State University  \nBeadle Scholar  \n\n| Research & Publications | Beacom College of Computer and Cyber Sciences |\n| --- | --- |\n| 2-2023\u003Cbr>MACHINE LEARNING APPLICATIONS IN MALWARE\u003Cbr>CLASSIFICATION: A METAANALYSIS LITERATURE REVIEW T.J. Nelson\u003Cbr>Austin O'Brien\u003Cbr>Dakota State University\u003Cbr>Cherie Noteboom\u003Cbr>Dakota State University\u003Cbr>Follow this and additional works at: [https://scholar.dsu.edu/ccspapers](https://scholar.dsu.edu/ccspapers)\u003Cbr> Part of the Computer and Systems Architecture Commons |  |\n\nRecommended Citation  \nNelson, 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.  \nThis 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).  \nMACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A META  \nANALYSIS  \nLITERATURE REVIEW  \nTjada Nelson, Austin O’Brien and Cherie Noteboom  \nBeacom College of Computer & Cyber Sciences, Dakota State University,  \nMadison, South Dakota  \nABSTRACT  \nWith 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.  \nThe 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.  \nKEYWORDS  \nMalware, Malware Classification, Machine Learning.  \n1. INTRODUCTION  \nMachine 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.  \nMachine 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  \nproduce a system that predicts future results. For example, supervised machine learning is the method to perform classification or face reco","cbCaiuJBPVQPFDNr","https://ap.wps.com/l/cbCaiuJBPVQPFDNr","pdf",1065458,1,13,"English","en",105,"# Abstract\n# Keywords\n# 1. 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