[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291117-105":59,"doc-detail-291117-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","evaluating-the-effectiveness-of-machine-learning-algorithms-in-detecting-phishing-attacks-msc-research-project","Evaluating the Effectiveness of Machine Learning Algorithms in Detecting Phishing Attacks - MSc Research Project","","Phishing remains a frequent and highly damaging cyber threat that leads to financial loss and information leakage. This paper designs and evaluates a machine-learning approach for detecting phishing URLs, using algorithms including Random Forest, SVM, k-NN, Naive Bayes, and Decision Tree, on an open-source feature dataset. Results highlight strong performance for Random Forest and SVM, with high precision, F1-score, specificity, accuracy, and ROC-AUC. The study also analyzes how feature selection and hyperparameters affect outcomes, and discusses limitations of static models on imbalanced datasets, proposing future deep learning extensions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluating-the-effectiveness-of-machine-learning-algorithms-in-detecting-phishing-attacks-msc-research-project/291117/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluating-the-effectiveness-of-machine-learning-algorithms-in-detecting-phishing-attacks-msc-research-project/291117.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-20","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main research objective of the study?","Question",{"text":113,"@type":114},"The study systematically evaluates and optimizes machine learning algorithms for detecting phishing attacks, focusing on how to achieve accurate detection through model and feature choices.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning algorithms are evaluated for phishing URL detection?",{"text":118,"@type":114},"The paper evaluates Random Forest, SVM, k-NN, Naive Bayes, and Decision Tree classifiers for categorizing phishing websites.",{"name":120,"@type":111,"acceptedAnswer":121},"What key performance outcomes are reported for Random Forest and SVM?",{"text":122,"@type":114},"The study reports high precision and F1-scores, very high specificity and accuracy, and ROC-AUC values above 0.99 for Random Forest and SVM.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291117,1789643499,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Evaluating the Effectiveness of Machine Learning Algorithms in Detecting Phishing  \nAttacks  \nMScResearchProject  \nMSC CYBER SECURITY  \nSUNIL KUMAR PRATURI  \nStudentID:X23242558  \nSchoolofComputing NationalCollegeofIreland  \nSupervisor: NIALL HEFFERNAN  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | SUNIL KUMAR PRATURI |\n| --- | --- |\n| Student ID: | X23242558 |\n| Programme: | MSC CYBER SECURITY |\n| Year: | 2024 |\n| Module: | MSc Research Project |\n| Supervisor: | NIALL HEFFERNAN |\n| Submission Due Date: | 12/12/2024 |\n| Project Title: | Evaluating the Effectiveness of Machine Learning Algorithms in Detecting Phishing Attacks |\n| Word Count: | 5863 |\n| Page Count: | 21 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | SUNIL KUMAR PRATURI |\n| --- | --- |\n| Date: | 12nd December 2024 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies). | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies). | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nEvaluating the Effectiveness of Machine Learning Algorithms in Detecting Phishing Attacks  \nSUNIL KUMAR PRATURI  \nX23242558  \nAbstract  \nPhishing continues to be one of the most frequent and dangerous types of cyber attacks that result in liquidated funds and leakage of information. This paper describes the design and assessment of a machine-learning algorithm for the detection of phishing URLs. The objective of the study was to learn and categorise phishing websites with the help of machine learning algorithms such as Random Forest classifier, SVM classifier, k-NN classifier, Na¨ıve Bayes classifier and Decision Tree classifier. The study comprised an open-source dataset of features, includingthe length of the URL, similarity of domains, and previous response records, among others. This work shows the effectiveness of Random Forest and SVM with a precision of 0.9814, F1-Scores 0.9705, Specificity: 0.99 and Accuracy of 0.9974 and ROC-AUC value of more than 0.99. The study also shows how feature selection and hyperparameters should be carefully conducted in order to achieve the best outcome. Furthermore, the study presents a discussion of these results for theoretical and empirical research and practical applications in cybersecurity. Despite high Accuracy in standard datasets in challenged scenarios, the static models fail in dealing with imbalanced datasets and cannot incorporate the more numerous, but irrelevant indicators. In conclusion the authors provide suggestions for potential future work based on the idea of introducing other deep learning methods in order to enhance and improve the result of the real-time testing of the presented models.  \n1 Introduction  \nPhishing emerged as one of the serious threats to cybersecurity as more than four million phishing attacks were reported in 2023 around the world Group (2023) . These are real scams: conning targets into providing","cbCairkgb0m38VLe","https://ap.wps.com/l/cbCairkgb0m38VLe","pdf",477074,23,"English","# Abstract\n# 1 Introduction\n## Research question and aims\n## Document structure","[{\"question\":\"What is the main research objective of the study?\",\"answer\":\"The study systematically evaluates and optimizes machine learning algorithms for detecting phishing attacks, focusing on how to achieve accurate detection through model and feature choices.\"},{\"question\":\"Which machine learning algorithms are evaluated for phishing URL detection?\",\"answer\":\"The paper evaluates Random Forest, SVM, k-NN, Naive Bayes, and Decision Tree classifiers for categorizing phishing websites.\"},{\"question\":\"What key performance outcomes are reported for Random Forest and SVM?\",\"answer\":\"The study reports high precision and F1-scores, very high specificity and accuracy, and ROC-AUC values above 0.99 for Random Forest and SVM.\"}]","Evaluating the Effectiveness of Machine Learning Algorithms in Detecting Phishing Attacks - MSc Research Project | PDF",58]