[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123787-en":3,"doc-seo-123787-105":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},123787,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Unveiling the Veiled - Unmasking Fileless Malware through Memory Forensics and Machine Learning","Recent advances in malware development have shifted attackers from file-based techniques to fileless malware, intensifying cybersecurity risk and reducing the effectiveness of traditional signature-based detection. This research presents a detection and mitigation framework that fuses memory forensics with machine learning. Volatile memory is captured via virtual machines and analyzed using the Volatility framework, while machine learning models automate classification. Random Forest achieves 93.33% overall accuracy with 87.5% TPR and zero FPR on public fileless datasets, and the system is supported by a user interface and AWS scalability, enabling efficient real-time defense.","Unveiling the Veiled: Unmasking Fileless Malware through Memory Forensics and Machine Learning  \nJyoshna Bejjam1*  \nAssociate Professor, CSE dept. Keshav Memorial Institute Of Technology  \nHyderabad, India  \ne-mail: [drjyoshnabejjam@gmail.com](drjyoshnabejjam@gmail.com)  \nBhuvanagiri2, Sai Devansh,  \nStudent, CSE dept. Keshav Memorial Institute Of Technology  \nHyderabad, India  \ne-mail: [saidevansh023@gmail.com](saidevansh023@gmail.com)  \nRecharla Divya Reddy3,  \nStudent, CSE dept. Keshav Memorial Institute Of Technology  \nHyderabad, India  \ne-mail: [recharladivyareddy@gmail.com](recharladivyareddy@gmail.com)  \nMandadi Vaishnavi4,  \nStudent, CSE dept. Keshav Memorial Institute Of Technology  \nHyderabad, India  \n[e-mail: ](e-mail: vaishnavi272012@gmail.com)[vaishnavi272012@gmail.com](e-mail: vaishnavi272012@gmail.com)  \nSravya Ravulakolla5,  \nStudent, CSE dept. Keshav Memorial Institute Of Technology  \nHyderabad, India  \ne-mail: [ravulakollasravya@gmail.com](ravulakollasravya@gmail.com)  \nAbstract-In recent times, significant advancements within the realm of malware development have dramatically reshaped the entire landscape. The reasons for targeting a system have undergone a complete transformation, shifting from file-based to fileless malware.Fileless malware poses a significant cybersecurity threat, challenging traditional detection methods. This research introduces an innovative approach that combines memory forensics and machine learning to effectively detect and mitigate fileless malware. By analyzing volatile memory and leveraging machine learning algorithms, our system automates detection.We employ virtual machines to capture memory snapshots and conduct thorough analysis using the Volatility framework. Among various algorithms, we have determined that the Random Forest algorithm is the most effective, achieving an impressive overall accuracy rate of 93.33% . Specifically, it demonstrates a True Positive Rate (TPR) of 87.5% while maintaining a zero False Positive Rate (FPR) when applied to fileless malware obtained from HatchingTriage, AnyRun, VirusShare, PolySwarm, and JoESandbox datasets. To enhance user interaction, a user-friendly graphical interface is provided, and scalability and processing capabilities are optimized through Amazon Web Services.Experimental evaluations demonstrate high accuracy and efficiency in detecting fileless malware. This framework contributes to the advancement of cybersecurity, providing practical tools for detecting against evolving fileless malware threats.  \nKeywords-Fileless malware, Cyber Security, machine learning, volatility.  \nI. INTRODUCTION (HEADING 1)  \nThe concept of detection raises when there is something inappropriate happens with the existing resources, Likewise we have come up with a detection tool to find the inappropriate behavior happening in a system. The reason behind this unusual behavior could be “MALWARE”  \nMalware: It refers to deliberately crafted computer programs designed to disrupt pre-existing computer applications. Malicious software may be employed for  \npurposes such as pilfering sensitive information, disrupting normal operations, and inflicting harm on computer systems.  \nIn the swiftly changing cybersecurity environment of today, the increasing sophistication of cyberattacks is surpassing the effectiveness of traditional malware detection methods. Fileless malware, in particular, poses a significant challenge due to its stealthy nature and ability to evade detection by residing solely in memory. Detecting and mitigating fileless malware requires innovative approaches that go beyond signature-based detection methods and delve into the realm of memory forensics and machine learning.  \nIn recent years, the prevalence of fileless malware attacks has escalated, leading to substantial financial losses,  \nreputational damage, and compromised data security for organizations across various sectors. Traditional malware detection mechanisms, such as signature-ba","cbCaipMeaZdCRdbb","https://ap.wps.com/l/cbCaipMeaZdCRdbb","pdf",464358,1,10,"English","en",105,"# Introduction\n## Problem background and motivation\n## Definition and characteristics of malware\n## Fileless malware vs. file-based malware","[{\"question\":\"Why is fileless malware difficult to detect with traditional methods?\",\"answer\":\"Fileless malware resides in volatile memory (RAM) and leverages legitimate system processes, leaving minimal traces on disk. This makes signature-based disk and file scanning less effective.\"},{\"question\":\"How does the proposed framework detect fileless malware?\",\"answer\":\"It captures memory snapshots using virtual machines, analyzes them with the Volatility framework, and applies machine learning for automated detection based on volatile-memory features.\"},{\"question\":\"Which machine learning algorithm performed best in the study, and what were the results?\",\"answer\":\"Random Forest was the most effective, reaching 93.33% overall accuracy, with a true positive rate of 87.5% and a zero false positive rate on datasets including HatchingTriage, AnyRun, VirusShare, PolySwarm, and JoESandbox.\"}]","Unveiling the Veiled - Unmasking Fileless Malware through Memory Forensics and Machine Learning | PDF",1785818564,25,{"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},"unveiling-the-veiled-unmasking-fileless-malware-through-memory-forensics-and-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/unveiling-the-veiled-unmasking-fileless-malware-through-memory-forensics-and-machine-learning/123787/",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 is fileless malware difficult to detect with traditional methods?","Question",{"text":75,"@type":76},"Fileless malware resides in volatile memory (RAM) and leverages legitimate system processes, leaving minimal traces on disk. This makes signature-based disk and file scanning less effective.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework detect fileless malware?",{"text":80,"@type":76},"It captures memory snapshots using virtual machines, analyzes them with the Volatility framework, and applies machine learning for automated detection based on volatile-memory features.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithm performed best in the study, and what were the results?",{"text":84,"@type":76},"Random Forest was the most effective, reaching 93.33% overall accuracy, with a true positive rate of 87.5% and a zero false positive rate on datasets including HatchingTriage, AnyRun, VirusShare, PolySwarm, and JoESandbox.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]