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URL-based features and multiple model training on a mixed dataset support an extensible system for phishing and zero-day identification, emphasizing incremental learning for near real-time updates using the PHIUSIIL and Mendeley datasets.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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 \nEmin Varghese  \nStudent ID: x23154250  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Arghir Nicolae Moldovan  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nStudent Name: Emin Varghese  \nStudent ID: X23154250  \nProgramme: MSc. Cybersecurity Year: 2024-25  \nModule: MSc. Research Project  \nSupervisor: Arghir Nicolae Moldovan  \nSubmission Due  \nDate: 12/12/2024  \nProject Title: Detection of Phishing URLs using Machine Learning  \nWord Count: 6690 words excluding references Page Count: 20  \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: Emin Varghese  \nDate: 12/12/2024  \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 foryour 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\nDetection of Phishing URLs using Machine Learning  \nEmin Varghese  \nx23154250  \nAbstract  \nPhishing websites are an increasing cybersecurity threat which puts both customers and business at risk as they compromise sensitive data. As phishing acts as the initial step in cyberattacks, early detection of these sites is important. Even though traditional methods, which rely on manual feature engineering are effective, they often find it difficult to catch up with the fishing tactics. This research emphasis on using machine learning (ML) to facilitate and enhance the detection process, improving its adaptability to rising threats. By reviewing URL-based features and training models on a mixed dataset, this study aims to make an innovative system that can easily identify phishing websites and zero-day attacks, improving the security measures and the decreasing the risks related to phishing attacks. PHIUSIIL and Mendeley datasets were preprocessed and then analyzed under six machine learning algorithms. The work also emphasizes the effectiveness of incremental learning solutions, especially for updating data in real-time. This work focuses on the application of machine learning (ML) for the detection of phishing URL. Using PHIUSIIL dataset and Mendeley dataset, a comparative analysis of URLs and content of different models was performed. The results prove the effectiveness of incremental learning strategies and bring focus on the need for strong and easily extendable methods adapted to the current threats.  \n1 Introduction  \n1.1 Background  \nThe digital evolution of the society driven by the accessibility and affordability of the internet which changed our engagement with essential services like banking, shopping, and education, meanwhile these advancements also opened the door to new vulnerabilities. The number of exploitations or cyber-attacks that occur on online platforms are increasing rapidly thereby compromising the sensitive or confidential data of groups or individuals. The phishing attack plays an i","cbCaiuLRZp4msfQt","https://ap.wps.com/l/cbCaiuLRZp4msfQt","pdf",625505,22,"English","# 1 Introduction\n## 1.1 Background\n## 1.2 Research Question\n## 1.3 Objectives\n## 1.4 Contributions","[{\"question\":\"What is the main research focus of the project?\",\"answer\":\"The project focuses on using machine learning to detect phishing URLs effectively, with attention to precision, scalability, and adaptability to real-time threats.\"},{\"question\":\"Why is early phishing URL detection important?\",\"answer\":\"Phishing websites are an increasing threat because they enable cyberattacks that compromise sensitive personal or business data, making early detection essential for reducing risk.\"},{\"question\":\"What datasets and learning approach are emphasized?\",\"answer\":\"The study preprocesses and analyzes the PHIUSIIL and Mendeley datasets and emphasizes incremental learning so the detection system can update data for real-time effectiveness.\"}]","Detection of Phishing URLs using Machine Learning - MSc Research Project - Cyber Security | PDF",55]