[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290532-105":59,"doc-detail-290532-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","detection-of-phishing-websites-using-machine-learning-msc-cybersecurity-research-project","Detection of Phishing Websites Using Machine Learning - MSc Cybersecurity Research Project","","Phishing attacks pose a significant threat to corporate and personal internet users by compromising personal data and financial security. As attackers evolve tactics, updated detection strategies become essential. Machine learning models learn patterns from large datasets, enabling more accurate and efficient detection than traditional rule-based methods. The research evaluates model performance using accuracy, precision, recall, and F1 score. Results indicate ensemble approaches such as Random Forest deliver robust predictions. Preprocessing supports scalability across varied phishing patterns, while deep learning can further improve accuracy.",{"@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/detection-of-phishing-websites-using-machine-learning-msc-cybersecurity-research-project/290532/",{"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/detection-of-phishing-websites-using-machine-learning-msc-cybersecurity-research-project/290532.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why are phishing attacks difficult to detect using traditional methods?","Question",{"text":113,"@type":114},"Rule-based systems and blacklists struggle to keep pace with sophisticated and constantly changing phishing techniques, so they often fail to identify new threats reliably.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How do machine learning models improve phishing detection?",{"text":118,"@type":114},"They learn patterns from large datasets, enabling more accurate and efficient predictions. By analyzing website features, models can identify malicious sites with a more adaptive approach.",{"name":120,"@type":111,"acceptedAnswer":121},"Which evaluation metrics were used to assess the models?",{"text":122,"@type":114},"Model performance was evaluated using accuracy, precision, recall, and the F1 score to analyze how effectively each approach detects phishing.","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},290532,1789641950,{"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":46,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":31,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Detection of Phishing Websites Using Machine  \nLearning  \nMSc Research Project MSc Cybersecurity  \nAkash Muralidharan  \nStudent ID: 23233192  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Vikas Sahni  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name: Akash Muralidharan  \nStudent ID: 23233192 Programme: MSc Cyber Security Year: 2024-2025  \nModule: MSc Practicum/Internship part 2  \nSupervisor: Vikas Sahni  \nSubmission Due  \nDate: 29th January 2025  \nProject Title: Detection of Phishing Websites Using Machine Learning  \nWord Count: 5796  \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.  \nSignature: Akash Muralidharan  \nDate: 29th January 2025  \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 isnot 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\nDetection of Phishing Websites Using Machine  \nLearning  \nAkash Muralidharan  \n23233192  \nAbstract  \nPhishing attacks pose a significant threat to both corporate and personal internet users, compromising personal data and financial security. As attackers evolve their tactics, thereis an urgent need for updated detection strategies. Machine learning models offer distinct advantages in detecting phishing attacks by learning patterns from large datasets and therefore providing more accurate and efficient predictions of evolving threats than traditional rule-based methods. This research examines how machine learning and AI can help prevent phishing attacks and by analysing website features, these models can effectively identify malicious sites, offering a more accurate and adaptive approach to detecting phishing threats. The performance of these models was evaluated on metrics like accuracy, precision, recall and F1 score, and this was done to obtain a better analysis of how these models perform. The results suggest that ensemble methods such as Random Forest are robust which gives high precision and recall for the prediction of phishing efforts. The system demonstrates the possibility to adapt to a variety of phishing patterns, and scales efficiently thanks to preprocessing. This solution represents an attainable and scalable phishing detection framework that promises practical action for organizations toward mitigating the defense against ever changing threats. Though the results are promising, more work is needed to integrate real time detection capabilities and extend the evaluation across larger and dynamic datasets. A huge gain in detection accuracy is also achieved by exploring deep learning models in this case.  \nKeywords – Phishing, Machine Learning, Logistic Regression, Random Forest Classifier.  \n1 Introduction  \nPhishing attacks are perhaps the most common and harmful form of cybercrime and present a serious risk to individuals, organizations and governments around the world. In an age when  \nthe web is increasing a","cbCaiaHpstBndc8Z","https://ap.wps.com/l/cbCaiaHpstBndc8Z","pdf",948789,"English","# 1 Introduction\n## Phishing attacks and risks\n## Limitations of rule-based detection\n## Motivation for ML and AI\n# 2 Research question and determinants\n## Dataset quality and diversity\n## Algorithms and feature engineering\n## Need for reliable, scalable systems","[{\"question\":\"Why are phishing attacks difficult to detect using traditional methods?\",\"answer\":\"Rule-based systems and blacklists struggle to keep pace with sophisticated and constantly changing phishing techniques, so they often fail to identify new threats reliably.\"},{\"question\":\"How do machine learning models improve phishing detection?\",\"answer\":\"They learn patterns from large datasets, enabling more accurate and efficient predictions. By analyzing website features, models can identify malicious sites with a more adaptive approach.\"},{\"question\":\"Which evaluation metrics were used to assess the models?\",\"answer\":\"Model performance was evaluated using accuracy, precision, recall, and the F1 score to analyze how effectively each approach detects phishing.\"}]","Detection of Phishing Websites Using Machine Learning - MSc Cybersecurity Research Project | PDF"]