[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120287-en":3,"doc-seo-120287-105":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":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":27,"seo_description":14,"update_tm":28,"read_time":29},120287,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Threat Forecasting - Machine Learning Applications in Next-Generation Identity Protection","Advanced identity-based attacks and evolving cyber threats make relying solely on defensive capabilities insufficient. The study evaluates predictive analytics using machine learning for proactive identity management and threat detection, comparing Decision Trees, Random Forests, Support Vector Machines, and a hybrid approach. Results show the hybrid model achieves the best performance across accuracy, precision, recall, and F1 score, enabling real-time dynamic detection and anomaly-based identity management that adapts to changing security environments.","e-ISSN:2582-7219  \nINTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH IN SCIENCE, ENGINEERING AND TECHNOLOGY  \nVolume 7, Issue 3 , March 2024  \nImpact Factor: 7.521  \n6381 907 438  6381 907 438  [ijmrset@gmail.com](ijmrset@gmail.com @ www.ijmrset.com)[ @](ijmrset@gmail.com @ www.ijmrset.com)[ www.ijmrset.com](ijmrset@gmail.com @ www.ijmrset.com)  \nInternational Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET)  \n| ISSN: [2582-7219 |](2582-7219 | www.ijmrset.com | Impact Factor:)[ ](2582-7219 | www.ijmrset.com | Impact Factor:)[www.ijmrset.com](2582-7219 | www.ijmrset.com | Impact Factor:)[ | Impact Factor:](2582-7219 | www.ijmrset.com | Impact Factor:) 7.521| Monthly, Peer Reviewed & Referred Journal|  \n| Volume 7, Issue 3, March 2024 |  \n| DOI:10.15680/IJMRSET.2024.0703003 |  \nThreat Forecasting-Machine Learning Applications in Next-Generation Identity Protection  \nGovindarajan Lakshmikanthan1, Sreejith Sreekandan Nair2  \nIndependent Researcher, Texas, USA1  \nIndependent Researcher, Texas, USA2  \nABSTRACT: Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the detection of both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score . The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems.  \nKEYWORDS: Cybersecurity, Predictive Analytics, Machine Learning, Hybrid Model, Threat Detection, Identity Management  \nI. INTRODUCTION  \nAs technology has advanced in the modern world, it has also brought about many benefits which include easy transfer of information, accessibility from anywhere, and improved user interface (Gómez-Carmona et al. 2023) . Transitions such as this one have however created new problems in the field of cybersecurity as new and much cleverer cyber threats emerge. Today's cyber security problems encompass data loss, digital impersonation, advanced persistent threats (APTs) and ransomware attacks which, among other things require proactive damage control (Alshamrani et al. 2019) . Such traditional techniques most of which are based on firm policies or regulations allowing manual tracking are notable to cope up with such threats and hence put the organizations at risk of attack (Saxena et al. 2020) . There is therefore evidence that demand for predictive and dynamic policies that address issues before they emerge is on the increase. Cybersecurity analytics has developed into one of the best solutions in defense management in this regard by utilizing the most creative algorithms available to prevent security threats.  \nThe Role of Predictive Analytics in Cybersecurity  \nPredictive analytics involves using statistical techniques, data mining, and machine learning to analyze historical and real-time data, uncover patterns, and predict future events (Kuppuswamy et al. 2024) . Within the context of cybersecurity, predictive analytics enables security systems to detect early signs of potential threats and identify vulnerabilities before they are exploited. This proactive approach marks a significant departure from traditional detection systems that r","cbCailXfEnKcaCnf","https://ap.wps.com/l/cbCailXfEnKcaCnf","pdf",1510407,1,10,"English","en",105,"# Introduction\n## The Role of Predictive Analytics in Cybersecurity\n## Machine Learning as a Catalyst for Proactive Security\n## Proactive Identity Management","[{\"question\":\"Why is threat forecasting important in next-generation identity protection?\",\"answer\":\"Because advanced identity-based attacks and complex cyber threats require proactive detection and management rather than reactive defenses after breaches occur.\"},{\"question\":\"Which machine learning models are assessed in the study?\",\"answer\":\"Decision Trees, Random Forests, Support Vector Machines (SVM), and a hybrid model combining supervised and unsupervised learning.\"},{\"question\":\"What makes the hybrid model effective for threat detection?\",\"answer\":\"It delivers the highest performance in accuracy, precision, recall, and F1 score, and supports real-time dynamic threat detection with anomaly-based identity management.\"}]","Threat Forecasting - Machine Learning Applications in Next-Generation Identity Protection | PDF",1785729249,25,{"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},"threat-forecasting-machine-learning-applications-in-next-generation-identity-protection","",{"@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/threat-forecasting-machine-learning-applications-in-next-generation-identity-protection/120287/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is threat forecasting important in next-generation identity protection?","Question",{"text":75,"@type":76},"Because advanced identity-based attacks and complex cyber threats require proactive detection and management rather than reactive defenses after breaches occur.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are assessed in the study?",{"text":80,"@type":76},"Decision Trees, Random Forests, Support Vector Machines (SVM), and a hybrid model combining supervised and unsupervised learning.",{"name":82,"@type":73,"acceptedAnswer":83},"What makes the hybrid model effective for threat detection?",{"text":84,"@type":76},"It delivers the highest performance in accuracy, precision, recall, and F1 score, and supports real-time dynamic threat detection with anomaly-based identity management.","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"]