[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120502-en":3,"doc-seo-120502-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120502,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Cyber-fraud detection methodology by using machine learning algorithms","Cyber fraud entails deceptive online practices such as phishing and fraudulent financial transactions, and its growth accelerates as digital communication expands. Despite improving prevention technologies, adversaries evolve, making reliable detection essential when preventive controls fail. The proposed methodology reduces online fraud by combining statistical and machine learning techniques—logistic regression, random forest, and naïve Bayes—on a carefully collected and fine-tuned dataset. User interaction and device profiles identify deviations associated with non-transactional fraud behaviors. Evaluation reports 100% accuracy using a unified multi-algorithm voting framework.","Cyber-fraud detection methodology by using machine learning  \nalgorithms  \nAhmed Abu-Khadrah1, Sahar Al-Washmi2, Ali Mohd Ali1, Muath Jarrah3  \n1Department of Electrical Engineering, College of Engineering Technology, Al-Balqa Applied University, Amman, Jordan 2College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia  \n3School of Computing, Skyline University College, Sharjah, United Arab Emirates  \nArticle history:  \nReceived Oct 16, 2024 Revised Mar 26, 2025 Accepted May 23, 2025  \nKeywords:  \nCyber fraud  \nCybercrime Logistic regression Naïve Bayes Random forest  \nCorresponding Author:  \nCybercrime covers a wide array of illegal online activities such as hacking and identity theft, while cyber fraud specifically involves deceptive practices like phishing and fraudulent financial transactions. The rise in technology and digital communication has exacerbated cyber fraud. Although prevention technologies are advancing, fraudsters continually adapt, making effective detection methods essential for identifying and addressing fraud when prevention fails. The proposed model aims to reduce online fraud through new detection algorithms. It utilizes statistical and machine learning techniques, including logistic regression, random forest, and naïve Bayes, to identify non-transactional fraud behaviors. By analyzing a meticulously collected and fine-tuned dataset, the study enhances detection capabilities beyond traditional transaction-focused approaches. The algorithms monitor user interactions and device characteristics to create profiles of normal behaviors and detect deviations indicative of fraud. The evaluation of proposed model showed 100% accuracy. A unified model incorporating all decision-making processes was used, leading to a voting phase and accuracy assessment. This approach consolidates multiple algorithms into a single framework, proving highly effective for comprehensive fraud detection. The research demonstrates the value of integrating machine learning techniques with real-world data to advance fraud detection and emphasizes the importance of continual adaptation to address evolving cyber threats.  \nThis is an open access article under the CC BY-SA license.  \nAhmed Abu-Khadrah  \nDepartment of Electrical Engineering, College of Engineering Technology, Al-Balqa Applied University Amman, Jordan  \nEmail: [a.abukhadrah@bau.edu.jo](a.abukhadrah@bau.edu.jo)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCyber-fraud research began in the 1990s. Early studies explored cyber-crime investigations and offered guides that addressed key concepts such as data privacy and computer security [1] . The rapid evolution of computer networks, with technologies like the cloud and the internet of things (IoT), has increased cyber security threats. It is essential for nations to adopt proactive defense measures, advanced technology, and guidance to protect their information and communication systems. The Kingdom, in particular, must ensure the confidentiality, integrity, and availability of its critical assets and infrastructure, aligning with its Vision 2030 by enhancing information sharing and establishing clear legal frameworks for data security and privacy [2] . In today's world, technology permeates every aspect of our lives, from banking and shopping to communication. However, this convenience comes with increased risks of fraud and identity theft. Learning to avoid becoming a victim of such crimes is crucial. While criminological discussions over  \nthe past 25 years have focused on the reduction of property crime, fraud has often been excluded from this analysis despite being a significant issue [3] . The rise of virtual currencies like Bitcoin, Ethereum, Ripple, and Litecoin has attracted malicious actors who use ransomware to obtain virtual currency. This ransomware infiltrates victims' systems, encrypting their files through sophisticated methods. This paper aims to analyze banking customer data from the","cbCaiiB5cSCriEag","https://ap.wps.com/l/cbCaiiB5cSCriEag","pdf",371716,1,"English","en",105,"# Keywords\n# Introduction\n## Cyber fraud scope and evolution\n## Machine learning approaches for fraud detection","[{\"question\":\"What problem does the methodology address?\",\"answer\":\"The methodology targets cyber fraud, including phishing and fraudulent financial transactions, by detecting fraud behaviors when prevention is insufficient.\"},{\"question\":\"Which machine learning algorithms are used in the proposed model?\",\"answer\":\"The study uses logistic regression, random forest, and naïve Bayes to identify fraud-related behaviors.\"},{\"question\":\"How is the model evaluated and what result is reported?\",\"answer\":\"A unified model consolidates decision-making processes and applies a voting phase for accuracy assessment, reporting 100% accuracy.\"}]","Cyber-fraud detection methodology by using machine learning algorithms | PDF",1785730388,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"cyber-fraud-detection-methodology-by-using-machine-learning-algorithms","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/cyber-fraud-detection-methodology-by-using-machine-learning-algorithms/120502/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the methodology address?","Question",{"text":74,"@type":75},"The methodology targets cyber fraud, including phishing and fraudulent financial transactions, by detecting fraud behaviors when prevention is insufficient.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms are used in the proposed model?",{"text":79,"@type":75},"The study uses logistic regression, random forest, and naïve Bayes to identify fraud-related behaviors.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the model evaluated and what result is reported?",{"text":83,"@type":75},"A unified model consolidates decision-making processes and applies a voting phase for accuracy assessment, reporting 100% accuracy.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]