[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-36980-en":3,"doc-seo-36980-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"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":13,"seo_description":14,"update_tm":27,"read_time":28},36980,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predictive Threat Detection using Temporal Event Correlation","Security Information and Event Management (SIEM) systems aggregate logs and alerts to identify and correlate threats, yet conventional deployments remain reactive and raise alarms after incidents occur. This paper proposes a Predictive Threat Detection Framework using Temporal Graph Neural Networks (TGNNs) to forecast attack paths ahead of escalation. By modeling time-evolving relationships among users, IPs, devices, and network connections, the framework detects early indicators of compromise and anticipates later kill-chain stages. Evaluation on intrusion datasets and simulated telemetry shows a 32% improvement in early detection accuracy and a 27% reduction in MTTR.","","cbCaig04Rf8m5Pgy","https://ap.wps.com/l/cbCaig04Rf8m5Pgy","pdf",2494062,1,9,"English","en",105,"# Introduction\n## Related Works\n### Traditional SIEM and Rule-Based Correlation\n### Machine Learning for Threat Detection\n### Graph-Based Security Analytics","[{\"question\":\"Why do traditional SIEM systems struggle with proactive cyber defense?\",\"answer\":\"Traditional SIEM approaches are inherently retrospective, generating alerts only after security incidents have already caused damage. They also rely on fixed rules or signatures that can miss multi-stage attacks that unfold across time.\"},{\"question\":\"How does the proposed TGNN framework enable predictive threat detection?\",\"answer\":\"The framework models entities such as users, IPs, devices, and connections as a temporal graph. It learns time-dependent relationships to forecast potential attack paths and anticipates subsequent stages in the kill chain.\"},{\"question\":\"What results does the framework achieve compared with rule-based correlation?\",\"answer\":\"Experiments using open-source intrusion datasets and simulated enterprise telemetry show a 32% improvement in early detection accuracy. The approach also reduces mean time-to-respond (MTTR) by 27% versus traditional rule-based methods.\"}]",1782947104,23,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":15,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"predictive-threat-detection-using-temporal-event-correlation",{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"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/predictive-threat-detection-using-temporal-event-correlation/36980/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"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","2026-07-08","2026-07-01",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why do traditional SIEM systems struggle with proactive cyber defense?","Question",{"text":74,"@type":75},"Traditional SIEM approaches are inherently retrospective, generating alerts only after security incidents have already caused damage. They also rely on fixed rules or signatures that can miss multi-stage attacks that unfold across time.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed TGNN framework enable predictive threat detection?",{"text":79,"@type":75},"The framework models entities such as users, IPs, devices, and connections as a temporal graph. It learns time-dependent relationships to forecast potential attack paths and anticipates subsequent stages in the kill chain.",{"name":81,"@type":72,"acceptedAnswer":82},"What results does the framework achieve compared with rule-based correlation?",{"text":83,"@type":75},"Experiments using open-source intrusion datasets and simulated enterprise telemetry show a 32% improvement in early detection accuracy. 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