[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118275-en":3,"doc-seo-118275-105":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},118275,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A hybrid machine learning approach for analysis of stegomalware","Purpose-driven cybersecurity study addresses the growing sophistication of malware authors, especially stealthy stegomalware that obscures indicators of compromise (IOC). The work proposes a hybrid machine learning framework that leverages both supervised and unsupervised techniques to support improved malware detection. Malware analysis is positioned as essential for uncovering hidden IOCs, understanding attacker intent, assessing damage severity, and exposing system vulnerabilities. After analysis, the produced output is intended to detect and counter attacks more effectively.","The current issue and full text archive of this journal is available on Emerald Insight at:  \n[https://www.emerald.com/insight/2690-6090.htm](https://www.emerald.com/insight/2690-6090.htm)  \nIJIEOM 5,2  \n104  \nReceived 12 October 2021 Revised 13 October 2022 30 January 2023 Accepted 15 February 2023  \nInternational Journal of Industrial Engineering and Operations Management  \nVol. 5 No. 2, 2023  \npp. 104-117  \nEmerald Publishing Limited e-ISSN: 2690-6104  \np-ISSN: 2690-6090  \nDOI 10. 1108/IJIEOM-01-2023-0011  \nA hybrid machine learning approach for analysis of stegomalware  \nPrudence Kadebu  \nDepartment of Information Systems, Women ’s University in Africa, Harare, Zimbabwe  \nRobert T.R. Shoniwa  \nZimbabwe Information and Communication Technology, Harare, Zimbabwe  \nKudakwashe Zvarevashe  \nVictoria Institute of Technology, Adelaide, Australia  \nAddlight Mukwazvure  \nDepartment of Computer Engineering, University of Zimbabwe, Harare, Zimbabwe  \nInnocent Mapanga  \nA Division of Zimbabwe Institution of Engineers, Zimbabwe Information and Communication Technology, Harare, Zimbabwe  \nNyasha Fadzai Thusabantu  \nA Division of Zimbabwe Institution of Engineers, Zimbabwe Information and Communication Technology, Harare, Zimbabwe and Department of Academics, Harare Institute of Technology, Belvedere, Zimbabwe, and  \nTatenda Trust Gotora  \nA Division of Zimbabwe Institution of Engineers, Zimbabwe Information and Communication Technology, Harare, Zimbabwe and Department of Computer Science, Midlands State University, Gweru, Zimbabwe  \nAbstract  \nPurpose – Given how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.  \nDesign/methodology/approach – This research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.  \n© Prudence Kadebu, Robert T.R. Shoniwa, Kudakwashe Zvarevashe, Addlight Mukwazvure, Innocent Mapanga, Nyasha Fadzai Thusabantu and Tatenda Trust Gotora. Published in International Journal of Industrial Engineering and Operations Management. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4 .0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and no commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at [http://creativecommons.org/licences/by/4.0/legalcode](http://creativecommons.org/licences/by/4.0/legalcode)  \nFindings – The tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results. Originality/value–Cyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophi","cbCaidozyM3n5MBY","https://ap.wps.com/l/cbCaidozyM3n5MBY","pdf",1664992,1,14,"English","en",105,"# Abstract\n## Purpose\n## Design/methodology/approach\n## Findings\n# Keywords\n# 1. Introduction\n## Malware threats and delivery vectors","[{\"question\":\"What problem does the proposed work target?\",\"answer\":\"It targets the difficulty of detecting stealthy malware, particularly stegomalware, where indicators of compromise (IOC) are hard to identify. The study aims to improve detection to support subsequent attack counteraction.\"},{\"question\":\"How does the approach combine machine learning methods?\",\"answer\":\"The research proposes a hybrid method that integrates unsupervised and supervised machine learning to enhance dynamic and static malware analysis. It also demonstrates how malware exploiting steganography can be exposed.\"},{\"question\":\"Why is malware analysis considered essential in this paper?\",\"answer\":\"Malware analysis is needed to reveal hidden IOC, understand the attacker’s goals and the severity of damage, and identify vulnerabilities within the system. Intelligent methods can help practitioners identify malware behavior and features, especially when malware hides IOC.\"}]","A hybrid machine learning approach for analysis of stegomalware | PDF",1785682760,35,{"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},"a-hybrid-machine-learning-approach-for-analysis-of-stegomalware","",{"@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/a-hybrid-machine-learning-approach-for-analysis-of-stegomalware/118275/",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","2026-08-02",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},"What problem does the proposed work target?","Question",{"text":75,"@type":76},"It targets the difficulty of detecting stealthy malware, particularly stegomalware, where indicators of compromise (IOC) are hard to identify. The study aims to improve detection to support subsequent attack counteraction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the approach combine machine learning methods?",{"text":80,"@type":76},"The research proposes a hybrid method that integrates unsupervised and supervised machine learning to enhance dynamic and static malware analysis. It also demonstrates how malware exploiting steganography can be exposed.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is malware analysis considered essential in this paper?",{"text":84,"@type":76},"Malware analysis is needed to reveal hidden IOC, understand the attacker’s goals and the severity of damage, and identify vulnerabilities within the system. Intelligent methods can help practitioners identify malware behavior and features, especially when malware hides IOC.","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"]