[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31240":3,"doc-seo-31240":26},{"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":19,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"table_of_contents":22,"faqs":23,"seo_title":13,"seo_description":14,"update_tm":24,"read_time":25},31240,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","AI Enhanced Digital Forensics: A Research Vision for Trustworthy, Explainable, and Human-Centered Forensic Intelligence","The paper addresses the pressure on digital forensic investigations created by rapidly growing data volumes, escalating cyber threats, and the reliance on digital evidence. It analyzes why traditional manual workflows struggle to scale across terabytes of heterogeneous data, cloud-native environments, and fileless attacks. It proposes a conceptual AI-Enhanced Digital Forensics research vision that augments human expertise with explainable, reproducible, and legally defensible AI, focused on triage, trustworthy decision-making, and LLM-supported reporting and chain-of-custody automation.","cbCaiq9c21ggnLFv","https://ap.wps.com/l/cbCaiq9c21ggnLFv","pdf",318147,1,5,"English","# Introduction\n# Motivation and Research Challenges\n## Key Research Challenges\n## Scalability vs. Forensic Soundness\n## Explainability\n## Reproducibility","[{\"question\":\"为什么传统数字取证流程难以满足当前调查需求？\",\"answer\":\"传统流程多依赖手工分析、规则工具和取证人员经验，难以在包含海量异构数据、云原生基础设施以及内存驻留/无文件攻击等场景中扩展，导致时间到首次发现变长、认知负担增加且分析结果不一致。\"},{\"question\":\"文中提出的AEDF-Lab研究愿景强调哪些核心目标？\",\"answer\":\"研究强调人本导向的AI增强取证：通过可解释、可复现、并具有法律可辩护性的方式支持取证分流、分析与报告。\"},{\"question\":\"AI在取证中的具体研究方向包含哪些方面？\",\"answer\":\"文中提出三类核心方向：AI辅助取证分流与工件优先级排序、用于可信取证决策的可解释AI框架、以及基于LLM的取证报告增强与链路证据保全自动化。\"}]",1779224492,13,{"code":4,"msg":27,"data":28},"ok",{"site_id":29,"language":30,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":24},105,"en","ai-enhanced-digital-forensics-a-research-vision-for-trustworthy-explainable-and-human-centered-forensic-intelligence","",{"@graph":34,"@context":84},[35,52,67],{"@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/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/ai-enhanced-digital-forensics-a-research-vision-for-trustworthy-explainable-and-human-centered-forensic-intelligence/31240/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-20","2026-05-19",true,{"@type":64,"interactionType":65,"userInteractionCount":19},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"为什么传统数字取证流程难以满足当前调查需求？","Question",{"text":74,"@type":75},"传统流程多依赖手工分析、规则工具和取证人员经验，难以在包含海量异构数据、云原生基础设施以及内存驻留/无文件攻击等场景中扩展，导致时间到首次发现变长、认知负担增加且分析结果不一致。","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"文中提出的AEDF-Lab研究愿景强调哪些核心目标？",{"text":79,"@type":75},"研究强调人本导向的AI增强取证：通过可解释、可复现、并具有法律可辩护性的方式支持取证分流、分析与报告。",{"name":81,"@type":72,"acceptedAnswer":82},"AI在取证中的具体研究方向包含哪些方面？",{"text":83,"@type":75},"文中提出三类核心方向：AI辅助取证分流与工件优先级排序、用于可信取证决策的可解释AI框架、以及基于LLM的取证报告增强与链路证据保全自动化。","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":29}]