[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122548-en":3,"doc-seo-122548-105":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},122548,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Comparative Analysis of Machine Learning Algorithms for Phishing Email Detection","Cyberattacks are accelerating alongside technological advancement, increasing the need for stronger detection and prevention methods. Focusing on e-mail phishing detection, the study evaluates machine learning approaches to distinguish secure from phishing messages. Support Vector Machine, Random Forest, Decision Tree, Logistic Regression, and CatBoost are analyzed using F1-score, recall, accuracy, and precision. Results indicate strong performance across all models, with SVM reaching perfect accuracy, underscoring advanced AI for evolving cyber threats.","NTU Journal of Engineering and Technology (2025) 4 (3): 9-14  \nDOI: [https://doi.org/10.56286/ntujet.v4i3](https://doi.org/10.56286/ntujet.v4i3)  \n|  |\n| --- |\n| Comparative Analysis of Machine Learning Algorithms for Phishing Email Detection\u003Cbr>Raweia S MohamedAli 1  , Razan Abdulhammed2\u003Cbr>1Computer Engineering Department, Technical Engineering College_Mosul, Northern Technical University,\u003Cbr>Mosul, 41001 , Iraq.\u003Cbr>2Technical Engineering College for Computer and AI. , Northern Technical University, Mosul-Iraq.\u003Cbr>[rawea.salem@ntu.edu.iq](rawea.salem@ntu.edu.iq), [rabdulhammed@ntu.edu.iq](rabdulhammed@ntu.edu.iq) |\n\nArticle Information  \nReceived: 19-02-2024, Revised: 22-05-2024, Accepted: 22-05-2024, Published online: 28-09-2025  \nCorresponding author:  \nName: Razan Abdulhammed  \nAffiliation: Northern Technical University [Email:](Email:rabdulhammed@ntu.edu.iq)[rabdulhammed@ntu.edu.iq](Email:rabdulhammed@ntu.edu.iq)  \n[Key Words:](Key Words:)  \nCybersecurity, Phishing Detection, Machine Learning, Artificial Intelligence.  \nA B S T R A C T  \nNowadays,The danger of cyberattacks grows as technology develops, requiring more advanced detection and prevention methods. With an emphasis on e-mail phishing detection, the study explores the use of machine learning (ML) to improve cybersecurity measures. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and CatBoost are among the ML models that are assessed to determine how well they can differentiate between secure and phishing e-mails. F1-score, recall, accuracy, and precision are among the evaluation measures. The results show that all models perform well, with SVM showing perfect accuracy. These findings highlight the importance of cutting-edge technologies in strengthening cybersecurity defenses against changing cyber threats.  \nTHIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE:  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n1. Introduction  \nThe rapid technological growth and digitalization has made more individuals rely on online platforms for daily requirements such as studying, shopping, transactions, and accessing services. It is now expected to open a smart device and quickly visit the websites of pharmacies, retailers, libraries, and educational institutions [1].Thus, E-services become more popular than in the last decade which lead to an increase of cyber attackers' dangers. Here, Attackers use weaknesses in the digital world to access and misuse sensitive user data, such as credit card information, names, phone numbers, and identifying details [2] . Phishing is a common technique used by cybercriminals through through various channels such as e-mail, SMS, or phone URLs [3] .  \nPhishing attacks intened to compromise personal data and internet accounts credintials. Attackers (Hackers) utilize various strategies, such as deceiving customers with authentic URLs resembling retail or banking websites or breaking into company networks without authorization to carry out more nefarious acts [4] . One type of Phishing attack, URL phishing, manipulates emails and URLs to trick consumers into thinking they communicate electronically with a reliable source [5] .  \nThe development of intelligent technologies, particularly machine learning (ML) becomes apparent in the face of these obstacles as a critical component in improving cybersecurity. Because of its many features, ML is spanning pattern recognition of adaptive security measures [6] .  \nMachine learning can reduce the requirement  \nfor human expertise in feature extraction and selection of phishing attempt detection ML models [7] . By using machine learning research were able to demonstrates the superior performance of these models, emphasizing its accuracy and efficiency [8] .  \nThe aggressive creation of anti-phishing technology based on machine learning, indicates an industry-wide effort to recognize and thwart new phishing at","cbCaiouS7te1ZBjm","https://ap.wps.com/l/cbCaiouS7te1ZBjm","pdf",810797,1,6,"English","en",105,"# Introduction\n# Related Works","[{\"question\":\"Which machine learning models are assessed for phishing email detection?\",\"answer\":\"The study evaluates SVM, Random Forest, Decision Tree, Logistic Regression, and CatBoost to classify emails as secure or phishing.\"},{\"question\":\"What evaluation metrics are used in the study?\",\"answer\":\"Performance is measured using F1-score, recall, accuracy, and precision.\"},{\"question\":\"What do the results show about the models’ effectiveness?\",\"answer\":\"All models perform well, and SVM achieves perfect accuracy in the reported results.\"}]","Comparative Analysis of Machine Learning Algorithms for Phishing Email Detection | PDF",1785811225,15,{"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},"comparative-analysis-of-machine-learning-algorithms-for-phishing-email-detection","",{"@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/comparative-analysis-of-machine-learning-algorithms-for-phishing-email-detection/122548/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are assessed for phishing email detection?","Question",{"text":75,"@type":76},"The study evaluates SVM, Random Forest, Decision Tree, Logistic Regression, and CatBoost to classify emails as secure or phishing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What evaluation metrics are used in the study?",{"text":80,"@type":76},"Performance is measured using F1-score, recall, accuracy, and precision.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results show about the models’ effectiveness?",{"text":84,"@type":76},"All models perform well, and SVM achieves perfect accuracy in the reported results.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]