[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31595":3,"doc-seo-31595":27},{"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,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31595,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",6,"Technology","X-ForensicNet: An Explainable Offline LLM Framework for Automated Windows Digital Forensics with Neuro-Symbolic Validation","Digital forensics investigation of Windows systems is often manual, slow, and dependent on expert interpretation of large forensic artifacts, while cloud-based LLM assistance raises privacy concerns and lacks the explainability needed for legal admissibility. X-ForensicNet proposes a fully offline framework combining local LLM inference, retrieval-augmented generation, and neuro-symbolic rule-based validation in a multi-agent architecture. It extracts Windows artifacts from disk images, structures results into JSON datasets, and generates transparent forensic reports, achieving 2.8× faster investigations and 96.3% accuracy on real and synthetic datasets.","cbCaivIxlXChpOkI","https://ap.wps.com/l/cbCaivIxlXChpOkI","pdf",892724,1,7,"English","en","# I. Introduction\n## A. Background and Motivation\n## B. Research Contributions\n# II. Related Work\n## A. Traditional Digital Forensics and Artifact Extraction\n## B. AI and Machine Learning in Digital Forensics","[{\"question\":\"Why does the proposed X-ForensicNet focus on an offline architecture for Windows digital forensics?\",\"answer\":\"The framework addresses privacy risks and compliance violations from cloud-based processing, and it supports complete offline operation for data-sensitive environments.\"},{\"question\":\"How does X-ForensicNet improve explainability and legal defensibility of forensic reports?\",\"answer\":\"It integrates neuro-symbolic rule-based validation with local LLM analysis, producing transparent reasoning trails suitable for judicial proceedings.\"},{\"question\":\"What performance gains and accuracy does X-ForensicNet report compared with manual analysis?\",\"answer\":\"The system accelerates investigations by 2.8× while improving accuracy to 96.3%, based on evaluations on real and synthetic forensic datasets.\"}]",1779743064,18,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"x-forensicnet-an-explainable-offline-llm-framework-for-automated-windows-digital-forensics-with-neuro-symbolic-validation","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/technology/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/x-forensicnet-an-explainable-offline-llm-framework-for-automated-windows-digital-forensics-with-neuro-symbolic-validation/31595/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-25",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Why does the proposed X-ForensicNet focus on an offline architecture for Windows digital forensics?","Question",{"text":73,"@type":74},"The framework addresses privacy risks and compliance violations from cloud-based processing, and it supports complete offline operation for data-sensitive environments.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does X-ForensicNet improve explainability and legal defensibility of forensic reports?",{"text":78,"@type":74},"It integrates neuro-symbolic rule-based validation with local LLM analysis, producing transparent reasoning trails suitable for judicial proceedings.",{"name":80,"@type":71,"acceptedAnswer":81},"What performance gains and accuracy does X-ForensicNet report compared with manual analysis?",{"text":82,"@type":74},"The system accelerates investigations by 2.8× while improving accuracy to 96.3%, based on evaluations on real and synthetic forensic datasets.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]