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The study targets a key gap: how obfuscation affects static machine learning (ML)-based malware detectors. It evaluates techniques such as encryption, code obfuscation, and polymorphism, rigorously measuring their influence on the precision and efficiency of ML detection algorithms. Findings support identifying current detector weaknesses and improving cyber defenses against disguised malware threats.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/analyzing-obfuscation-techniques-for-evasion-a-case-study-on-machine-learning-based-malware-detection/290530/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/analyzing-obfuscation-techniques-for-evasion-a-case-study-on-machine-learning-based-malware-detection/290530.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the research address about malware obfuscation?","Question",{"text":113,"@type":114},"Malware obfuscation conceals a program’s real characteristics and operations, increasing the difficulty and time required for analysis and reverse engineering, which can help malware evade detection and prevention methods.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which obfuscation techniques and detector type does the study focus on?",{"text":118,"@type":114},"The project examines techniques such as encryption, code obfuscation, and polymorphism, and it assesses their impact on static ML-based malware detectors.",{"name":120,"@type":111,"acceptedAnswer":121},"What outcomes does the study aim to achieve for cybersecurity defenses?",{"text":122,"@type":114},"It evaluates how obfuscation weakens ML detectors by measuring changes in precision and efficiency, then uses the results to suggest improvements that strengthen resistance against advanced evasion attacks.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290530,1789641946,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Analyzing Obfuscation Techniques for Evasion: A Case Study on Machine Learning-based Malware Detection  \nMSc Research Project Artificial Intelligence  \nTugrul Un  \nStudent ID: 22181695  \nSchool of Computing National College of Ireland  \nSupervisor: Anh Duong Trinh  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Tugrul Un |\n| --- | --- |\n| Student ID: | 22181695 |\n| Programme: | Artificial Intelligence |\n| Year: | 2024 |\n| Module: | MSc Research Project |\n| Supervisor: | Anh Duong Trinh |\n| Submission Due Date: | 05/01/2024 |\n| Project Title: | Analyzing Obfuscation Techniques for Evasion: A Case Study on Machine Learning-based Malware Detection |\n| Word Count: | 3140 |\n| Page Count: | 13 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: |  |\n| --- | --- |\n| Date: | 31st January 2024 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nAnalyzing Obfuscation Techniques for Evasion: A Case Study on Machine Learning-based Malware  \nDetection  \nTugrul Un  \n22181695  \nAbstract  \nThis research focuses on the intricate domain of malware obfuscation, a method utilized by malicious people to conceal the actual characteristics and operations of their code, thereby rendering its analysis and reverse engineering more difficult and time-consuming. Consequently, this enhances the malware’s capacity to elude detection and preventive methods. An area that is crucial but not thoroughly examined is the effect of these obfuscation techniques on malware detectors that rely on Machine Learning (ML) . The primary objective of the research is to carry out a thorough examination of several obfuscation techniques, such as encryption, code obfuscation, and polymorphism, employed by attackers to conceal their malware. Having this comprehension is crucial for evaluating the present and possible future scenario of cyber risks. Additionally, the project aims to assess the impact of different obfuscation approaches on Static ML-based malware detectors. This study rigorously evaluates the impact of obscuring malware on the precision and efficiency of machine learning-based detection algorithms. An assessment of this nature is essential for uncovering the current capabilities and constraints of machine learning detectors when faced with advanced obfuscation techniques. Finally, the study aims to improve the identification and prevention methods in cybersecurity by identifying the weaknesses of machine learning-based malware detectors when faced with obfuscation attacks. The objective is to make a significant contribution to the area by suggesting ways that can strengthen the resistance of machine learningbased detectors against sophisticated and disguised malware threats. This would enhance cybersecurity defenses against constantly emergin","cbCaim5VFSwBLoqL","https://ap.wps.com/l/cbCaim5VFSwBLoqL","pdf",915077,15,"English","# Abstract\n# 1 Introduction","[{\"question\":\"What problem does the research address about malware obfuscation?\",\"answer\":\"Malware obfuscation conceals a program’s real characteristics and operations, increasing the difficulty and time required for analysis and reverse engineering, which can help malware evade detection and prevention methods.\"},{\"question\":\"Which obfuscation techniques and detector type does the study focus on?\",\"answer\":\"The project examines techniques such as encryption, code obfuscation, and polymorphism, and it assesses their impact on static ML-based malware detectors.\"},{\"question\":\"What outcomes does the study aim to achieve for cybersecurity defenses?\",\"answer\":\"It evaluates how obfuscation weakens ML detectors by measuring changes in precision and efficiency, then uses the results to suggest improvements that strengthen resistance against advanced evasion attacks.\"}]","Analyzing Obfuscation Techniques for Evasion - A Case Study on Machine Learning-based Malware Detection | PDF",38]