[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118695-en":3,"doc-seo-118695-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118695,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Enhancing Cybersecurity Threat Detection Using Machine Learning - A Comprehensive Review","Cybersecurity underpins today’s digital infrastructure for protecting payment systems, government services, and business continuity. This review explains how machine learning (ML) techniques can process large volumes of security data, automate threat analysis, and accelerate responses. It examines the role of ML-based security and threat detection, highlighting classifiers such as SVM, Decision Trees, Random Forests, and XGBoost. Performance evaluation is discussed using metrics including confusion, recall, F1-score, accuracy, precision, and time complexity across benchmark datasets.","ENHANCING CYBERSECURITY THREAT DETECTION USING MACHINE LEARNING: A COMPREHENSIVE REVIEW  \nP. Somasundari1*, V. Kavitha2  \nAssistant Professor, Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India 1  \nProfessor, Department of Computer Science and Engineering, University College of Engineering, Kancheepuram, Tamil Nadu, India2  \n[plsomasundari@gmail.com](plsomasundari@gmail.com1)[1](plsomasundari@gmail.com1), [kavinayav@gmail.com](kavinayav@gmail.com2)[2](kavinayav@gmail.com2)  \nReceived: 24 July 2025, Revised: 06 November 2025, Accepted: 13 November 2025  \n*Corresponding Author  \nABSTRACT  \nCybersecurity forms the backbone of digital infrastructure that protects overstretched payment systems, governmental operations, and business continuity today. With machine learning (ML) techniques, it can help analyze a large amount of data and improve cyber-security. It’s tough to quantify how effective the ML-based cybersecurity system is, especially when we theorize it. This review paper talks about the significant role of ML in security, threat detection and security measures. Using machine learning algorithms helps in cybersecurity as they make the system automatic and fast. We can implement a threat detection security model using widely used ML algorithms. For classification purposes, we have Support Vector Machines (SVM), Decision Trees (DT), Random forests (RF), and Adaptive and Extreme gradient boosting (XGBoost). This review paper proposes ML algorithms for the implementation of cybersecurity with some practical application demonstrations. Machine learning algorithms can provide valuable analytics to help bolster security and reduce threats. We assess the accuracy of threat detection in network security by utilizing a set of formulas based on confusion, recall, F1-score, time complexity, accuracy and precision. This review synthesizes algorithmic performance across benchmark datasets (CICIDS2017 NSLKDD UNSW-NB15) to identify significant gaps in previous ML-based cybersecurity frameworks. The results demonstrate the superior precision (90. 8 percent) and scalability of XGBoost.  \nKeywords: Cyber security, threat detection, machine learning, Adaptive Boosting, XGBoost, SVM, RF and accuracy.  \n1. Introduction  \nDistributed energy resources (DERs) refer to various data analytics related to energy use and performance (Okoli et al., 2024) . We must make sure that the network connections are in order and scattered. Artificial intelligence (AI) and machine learning (ML) are being used more to enhance cybersecurity through automated threat detection and response. Algorithms like SVM Random Forest and XGBoost are better at identifying anomalies and intrusion patterns than traditional systems. However, problems with current ML-based approaches still exist such as high computational cost limited adaptability to changing attacks and lack of interpretability (Katiyar et al., 2024) . Cyber-physical security ensures a secure level of performance, maintaining high runtime standards. It prioritizes reliability and fosters trust, enhancing efficiency in energy practices. The cyber threat is significantly affecting financial issues for individuals, and more economic aspects of the process. To resolve the technical issues, early detection of the trends online is essential.  \nExplainable Artificial Intelligence (XAI) methods detect various attacks in cybersecurity issues, analyze more data, and compare the previous day’s data with current performance (AlShehari et al., 2024) . They identify unknown data to detect unpredictable values and facilitate decision-making regarding network connections and performance (Yeboah-Ofori et al., 2021) . The main detection in a cybersecurity process is anomalies; malware is initially verifying the detection, and testing validation of the performance is more accurate for the outcomes. The prevalent method used for final data relates to the prevention of the tec","cbCaiux8OTmtNnfN","https://ap.wps.com/l/cbCaiux8OTmtNnfN","pdf",1997451,1,23,"English","en",105,"# Introduction\n## ML and AI for automated threat detection\n## Explainable AI and anomaly-focused detection\n## Network security procedures and model classification\n## Impacts of cyber threats on individuals and organizations\n## Challenges in current ML-based cybersecurity approaches\n## Evaluation metrics and benchmark datasets","[{\"question\":\"Which machine learning algorithms are used for threat detection in the review?\",\"answer\":\"The review highlights Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and gradient boosting using XGBoost for classification and threat detection.\"},{\"question\":\"How does the review evaluate the effectiveness of ML-based threat detection?\",\"answer\":\"Effectiveness is assessed using metrics derived from the confusion matrix and additional measures such as recall, F1-score, accuracy, precision, and time complexity.\"},{\"question\":\"Which benchmark datasets are used to compare algorithmic performance?\",\"answer\":\"The review synthesizes results across benchmark datasets including CICIDS2017, NSLKDD, and UNSW-NB15 to identify gaps in prior ML-based cybersecurity frameworks.\"}]","Enhancing Cybersecurity Threat Detection Using Machine Learning - A Comprehensive Review | PDF",1785684925,58,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhancing-cybersecurity-threat-detection-using-machine-learning-a-comprehensive-review","",{"@graph":36,"@context":86},[37,54,69],{"@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/enhancing-cybersecurity-threat-detection-using-machine-learning-a-comprehensive-review/118695/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning algorithms are used for threat detection in the review?","Question",{"text":76,"@type":77},"The review highlights Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and gradient boosting using XGBoost for classification and threat detection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the review evaluate the effectiveness of ML-based threat detection?",{"text":81,"@type":77},"Effectiveness is assessed using metrics derived from the confusion matrix and additional measures such as recall, F1-score, accuracy, precision, and time complexity.",{"name":83,"@type":74,"acceptedAnswer":84},"Which benchmark datasets are used to compare algorithmic performance?",{"text":85,"@type":77},"The review synthesizes results across benchmark datasets including CICIDS2017, NSLKDD, and UNSW-NB15 to identify gaps in prior ML-based cybersecurity frameworks.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]