[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117279-en":3,"doc-seo-117279-105":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},117279,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION - A METAANALYSIS LITERATURE REVIEW","This study uses text mining and bibliometrics to review literature on the evolution of malware classification methods driven by machine learning. Articles published from 2008 to 2022 are analyzed to assess growth, identify the most common machine-learning applications for malware classification, and summarize resulting outcomes. From 2,186 peer-reviewed records, quantitative and qualitative evidence is derived with statistical methods and N-gram analysis, enabling the study to map trends across both malware classification and machine learning research.","Dakota State University  \nBeadle Scholar  \n\n| Research & Publications | College of Business and Information Systems |\n| --- | --- |\n\n2-2023  \nMACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITERATURE REVIEW Cherie Noteboom  \nAustin O'Brien  \nT.J. Nelson  \nFollow this and additional works at: [https://scholar.dsu.edu/bispapers](https://scholar.dsu.edu/bispapers)  \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 recognition. Unsupervised machine learning takes data and tries to identify clusters to classify similar or related data points and insights. This machine learning type builds recommendation systems, anomaly detection, and tracking buy habits in customer transactions. Reinforcement machine learning allows the system to operate and then notify it when it makes mistakes, so it learns to avoid them. This machine learning type is used to create video game AI and operation simulations. Machine learning has proved to be very useful in solving real-world problems in all these application types.  \nA significant problem in cybersecurity is malware which plays a major role in cybercrime. AV","cbCaitDXCt1k1vGJ","https://ap.wps.com/l/cbCaitDXCt1k1vGJ","pdf",998367,1,13,"English","en",105,"# Abstract\n# Introduction\n## Machine learning fundamentals and categories\n## Malware background and cybersecurity impact\n## Malware analysis approaches: static vs dynamic\n## Static analysis techniques and their role in classification\n# Methods and research questions\n## RQ1: growth of machine learning applications\n## RQ2: most common applications for malware classification\n## RQ3: outcomes of common application types\n# Findings: common machine learning methods and trends\n# Conclusion","[{\"question\":\"What is the main goal of this meta-analysis literature review?\",\"answer\":\"It reviews how machine learning has evolved for malware classification from 2008 to 2022, focusing on growth, common application types, and their outcomes.\"},{\"question\":\"Which research questions does the study address?\",\"answer\":\"RQ1 examines whether machine learning for malware classification is growing, RQ2 identifies the most common application type, and RQ3 summarizes the outcomes of those common applications.\"},{\"question\":\"What standard machine learning methods are reported as common in malware classification?\",\"answer\":\"The study highlights support vector machines and random forests as standard methods used to detect maliciousness or categorize malware by 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