[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122232-en":3,"doc-seo-122232-105":30,"detail-sidebar-cat-0-en-105":91},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},122232,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING","Malware attacks threaten digital systems by enabling data breaches and financial losses, and the growing diversity of malware techniques reduces the effectiveness of traditional signature-based approaches. This study improves malware detection by combining ensemble learning with feature selection based on Information Gain. Experiments use Random Forest, Gradient Boosting, XGBoost, and AdaBoost on datasets containing both goodware and malware samples sourced from VirusTotal and VxHeaven. Results indicate Gradient Boosting enhanced by Information Gain achieves the highest accuracy of 99.1%, improving detection effectiveness while lowering computational requirements, supporting optimization of digital security systems.","IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING  \nArsabilla Ramadhani1, Fauzi Adi Rafrastara*2, Salma Rosyada3, Wildanil Ghozi4, Waleed Mahgoub  \nOsman5  \n1,2,3,4Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia  \n5Mathematics Department, College of Education, Sudan University of Science and Technology, Sudan [Email:](Email:1111202113384@mhs.dinus.ac.id)[1](Email:1111202113384@mhs.dinus.ac.id)[111202113384@mhs.dinus.ac.id](Email:1111202113384@mhs.dinus.ac.id), [2](2fauziadi@dsn.dinus.ac.id)[fauziadi@dsn.dinus.ac.id](2fauziadi@dsn.dinus.ac.id), [3](3111202113382@mhs.dinus.ac.id)[111202113382@mhs.dinus.ac.id](3111202113382@mhs.dinus.ac.id), [4](4wildanil.ghozi@dsn.dinus.ac.id)[wildanil.ghozi@dsn.dinus.ac.id](4wildanil.ghozi@dsn.dinus.ac.id), [5](5waleedmo@sustech.edu)[waleedmo@sustech.edu](5waleedmo@sustech.edu)  \n(Article received: October 14, 2024; Revision: November 22, 2024; published: December 29, 2024)  \nAbstract  \nMalware attacks pose a serious threat to digital systems, potentially causing data and financial losses. The increasing complexity and diversity of malware attack techniques have made traditional detection methods ineffective, thus AI-based approaches are needed to improve the accuracy and efficiency of malware detection, especially for detecting modern malware that uses obfuscation techniques. This study addresses this issue by applying ensemble-based machine learning algorithms to enhance malware detection accuracy. The methodology used involves Random Forest, Gradient Boosting, XGBoost, and AdaBoost, with feature selection using Information Gain. Datasets from VirusTotal and VxHeaven, including both goodware and malware samples. The results show that Gradient Boosting, strengthened with Information Gain, achieved the highest accuracy of 99. 1%, indicating a significant improvement in malware detection effectiveness. This study demonstrates that applying Information Gain to Gradient Boosting can improve malware detection accuracy while reducing computational requirements, contributing significantly to the optimization of digital security systems.  \nKeywords: Ensemble-based Algorithms, Gradient Boosting, Information Gain, Machine Learning, Malware Detection.  \n1. INTRODUCTION  \nMalware, short for \"malicious software,\" refers to software specifically designed to harm, disrupt, or gain unauthorized access to computer systems [1] . The diversity of malware types, including viruses, worms, trojans, ransomware, and spyware, presents significant challenges for detection systems. Each operates in distinct ways to compromise data security [2], often causing severe consequences, such as network-wide infections, data theft, system damage, and the loss of critical information [3] .  \nA notorious example of malware is WannaCry, a ransomware that exploits vulnerabilities in the Windows operating system. WannaCry encrypts victims' data and demands a ransom in Bitcoin for its decryption. The attack had a massive impact on hospitals, government organizations, and companies globally, forcing them to halt operations due to the inability to access critical data. This attack resulted in billions of dollars in losses and exposed significant weaknesses in global cybersecurity systems [4] .  \nMore recently, other soophisticated malware such as Emotet have emerged, evolving into complex threats. Initially recognized as a banking trojans, these malware variant now serve as delivery vehicles for other harmful software, spreading through malicious email attachments disguised as legitimate  \ndocuments or links. Once a device is infected, these trojans can steal sensitive information, including passwords, financial data, and personal details, amplifying the potential damge. Emotet, in particular, has been widely used in high-profile cyberattacks, significanly amplifying the potential damage. These attacks have targeted sectors such as healthcare and government, res","cbCait6zTiC1zIit","https://ap.wps.com/l/cbCait6zTiC1zIit","pdf",1059934,1,14,"English","en",105,"# 1. Introduction\n## 1.1 Problem background and challenges\n## 1.2 Role of machine learning and ensemble methods\n## 1.3 Research aims and evaluation focus","[{\"question\":\"Why do traditional malware detection methods become ineffective?\",\"answer\":\"Traditional signature-based methods can miss new or unseen malware because minor code changes and advanced evasion techniques like obfuscation and encryption reduce detectability.\"},{\"question\":\"What combination does the study use to improve malware detection?\",\"answer\":\"The study applies ensemble-based machine learning algorithms and performs feature selection using Information Gain to strengthen classification performance.\"},{\"question\":\"Which algorithm achieved the best reported accuracy and what was the value?\",\"answer\":\"Gradient Boosting enhanced with Information Gain achieved the highest accuracy, reported as 99.1%.\"}]","IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING | PDF",1785809544,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improving-malware-detection-using-information-gain-and-ensemble-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improving-malware-detection-using-information-gain-and-ensemble-machine-learning/122232/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do traditional malware detection methods become ineffective?","Question",{"text":75,"@type":76},"Traditional signature-based methods can miss new or unseen malware because minor code changes and advanced evasion techniques like obfuscation and encryption reduce detectability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What combination does the study use to improve malware detection?",{"text":80,"@type":76},"The study applies ensemble-based machine learning algorithms and performs feature selection using Information Gain to strengthen classification performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm achieved the best reported accuracy and what was the value?",{"text":84,"@type":76},"Gradient Boosting enhanced with Information Gain achieved the highest accuracy, reported as 99.1%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]