[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31035":3,"doc-seo-31035":21},{"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,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"update_tm":20},31035,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Detecting Fileless Malware through Memory Forensics with Recurrent Neural Networks","Fileless malware executes solely in volatile memory, using legitimate system utilities to avoid signature-based and static analysis defenses. This work introduces a detection framework that combines memory forensics with recurrent neural networks to model temporal dependencies that traditional approaches miss. Forensic and behavioral features extracted from memory capture transient process activity, memory-resident module behavior, and short-lived network interactions. Experiments compare multiple RNN architectures and show Bidirectional LSTM delivers the strongest results, with accuracy 0.93 and recall 1.00, highlighting behavioral memory indicators as reliable for in-memory threat detection.","cbCaitmRH9X03vzt","https://ap.wps.com/l/cbCaitmRH9X03vzt","pdf",2716900,1,1778533225,{"code":4,"msg":22,"data":23},"ok",{"site_id":24,"language":25,"slug":26,"title":13,"keywords":27,"description":14,"schema_data":28,"social_meta":62,"head_meta":64,"extra_data":66,"updated_unix":20},105,"en","detecting-fileless-malware-through-memory-forensics-with-recurrent-neural-networks","",{"@graph":29,"@context":61},[30,47],{"@type":31,"itemListElement":32},"BreadcrumbList",[33,37,41,44],{"item":34,"name":35,"@type":36,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":38,"name":39,"@type":36,"position":40},"https://docshare.wps.com/document/","Document",2,{"item":42,"name":12,"@type":36,"position":43},"https://docshare.wps.com/document/research-report/",3,{"item":45,"name":13,"@type":36,"position":46},"https://docshare.wps.com/document/detecting-fileless-malware-through-memory-forensics-with-recurrent-neural-networks/31035",4,{"url":45,"name":13,"@type":48,"author":49,"headline":13,"publisher":51,"fileFormat":54,"description":14,"dateModified":55,"datePublished":55,"encodingFormat":54,"isAccessibleForFree":56,"interactionStatistic":57},"DigitalDocument",{"name":9,"@type":50},"Person",{"url":34,"name":52,"@type":53},"DocShare","Organization","application/pdf","2026-05-11",true,{"@type":58,"interactionType":59,"userInteractionCount":4},"InteractionCounter",{"@type":60},"ViewAction","https://schema.org",{"og:url":45,"og:type":63,"og:title":13,"og:site_name":52,"og:description":14},"article",{"robots":65,"canonical":45},"index,follow",{"doc_id":7,"site_id":24}]