[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123198-en":3,"doc-seo-123198-105":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":4,"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},123198,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",6,"Technology","Proactive Cybersecurity - Predictive Analytics and Machine Learning for Identity and Threat Management","Advanced identity-based attacks and evolving cyber threats make defensive capabilities insufficient on their own. This study evaluates predictive analytics powered by machine learning for proactive identity management and threat detection, comparing Decision Trees, Random Forests, Support Vector Machines, and a hybrid model combining supervised and unsupervised learning. The hybrid approach achieves the strongest results across accuracy, precision, recall, and F1 score. Its adaptability supports real-time dynamic threat detection and anomaly-driven identity management.","e-ISSN:2582-7219  \nINTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH IN SCIENCE, ENGINEERING AND TECHNOLOGY  \nVolume 7, Issue 12 , December 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)  \n© 2024 IJMRSET | Volume 7, Issue 12, December 2024| DOI: 10.15680/IJMRSET.2024.0712005  \nProactive Cybersecurity: Predictive Analytics and Machine Learning for Identity and Threat Management  \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 respond only after an attack has occurred. By applying predictive analytics, organizations can monitor behavioral patterns, assess risks, and take preemptive actions to mitigate threats, improving their overall security posture.  \nMachine Learning as a Catalyst for Proactive Security  \nMachine learning (ML) has become an essential tool in enhancing predictive analytic","cbCaiuWaqnRCiKLi","https://ap.wps.com/l/cbCaiuWaqnRCiKLi","pdf",1539157,1,11,"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 are traditional cybersecurity defenses insufficient against modern threats?\",\"answer\":\"Modern environments see more advanced identity-based attacks and complex threats, while many traditional techniques rely on static policies and manual tracking, increasing the risk of compromise.\"},{\"question\":\"Which machine learning models are evaluated for threat detection?\",\"answer\":\"The study compares Decision Trees, Random Forests, Support Vector Machines (SVM), and a hybrid model that integrates supervised and unsupervised learning.\"},{\"question\":\"What makes the hybrid model most effective in the study?\",\"answer\":\"The hybrid model achieves the highest performance using accuracy, precision, recall, and F1 score, and it supports real-time dynamic threat detection with anomaly-based identity management.\"}]","Proactive Cybersecurity - Predictive Analytics and Machine Learning for Identity and Threat Management | PDF",1785815171,28,{"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},"proactive-cybersecurity-predictive-analytics-and-machine-learning-for-identity-and-threat-management","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/proactive-cybersecurity-predictive-analytics-and-machine-learning-for-identity-and-threat-management/123198/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are traditional cybersecurity defenses insufficient against modern threats?","Question",{"text":75,"@type":76},"Modern environments see more advanced identity-based attacks and complex threats, while many traditional techniques rely on static policies and manual tracking, increasing the risk of compromise.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated for threat detection?",{"text":80,"@type":76},"The study compares Decision Trees, Random Forests, Support Vector Machines (SVM), and a hybrid model that integrates supervised and unsupervised learning.",{"name":82,"@type":73,"acceptedAnswer":83},"What makes the hybrid model most effective in the study?",{"text":84,"@type":76},"The hybrid model achieves the highest performance using accuracy, precision, recall, and F1 score, and it 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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]