[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122136-en":3,"doc-seo-122136-105":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},122136,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Web Application to Detect Phishing Links Using Machine Learning Approach","Covid-19 accelerated the shift to remote work and learning, increasing exposure to cyber crime, with phishing standing out as a highly effective technique for stealing personal information. Attackers use malicious emails, texts, and phone calls to lure victims and obtain identification data, passwords, credit and debit card details, and usernames, enabling financial loss or identity theft. Despite antivirus and phishing detection tools, phishing remains widespread. Blacklisting based on precompiled malicious URL lists struggles to detect new phishing links, while machine-learning approaches may achieve good accuracy but still generate notable false alarms. This study designs and implements a web-based phishing link detection tool using an ML deep learning model with 94.27% accuracy, supported by heuristic rules and blacklist capabilities, and recommends ongoing training, feature reengineering, and API-enabled IoT endpoint interaction control.","Electronic Thesesand Dissertations  \n2023  \nA Web application to detect phishing links using machine learning approach.  \nKimani, Benson Maina  \nSchool of Computing and Engineering Sciences Strathmore University  \nRecommendedCitation  \nKimani, B. M. (2023) . A Web application to detect phishing links using machine learning approach [Strathmore University] . [http://hdl.handle.net/11071/13531](http://hdl.handle.net/11071/13531)  \nFollow this andadditional works at:  [http://hdl.handle.net/11071/13531](http://hdl.handle.net/11071/13531)  \nA Web Application to Detect Phishing Links Using Machine Learning Approach  \nBy  \nBenson Maina Kimani  \n147493  \nMaster of Science in Information Technology  \nA Web Application to Detect Phishing Links Using Machine Learning Approach  \nBy  \nBenson Maina Kimani  \n147493  \nSubmitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Information Technology at Strathmore University  \nSchool of Engineering and Computing Sciences (SCES) Strathmore University  \nNairobi, Kenya.  \nJuly, 2023  \nThis thesis is available for Library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement.  \nDeclaration and Approval  \nDeclaration  \nI declare that this work has not been previously submitted and approved for the award of a degree by this or any other University. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself.  \n© No part of this thesis may be reproduced without the permission of the author and Strathmore University  \nStudent’s Name: Benson Maina Kimani  \nSign:   Date:  05/06/2023   \nApproval  \nThe thesis of Benson Maina Kimani was reviewed and approved for examination by the following:  \nDr Nelson Odunga Ochieng,  \nSchool of Computing & Engineering Sciences, Strathmore University  \nDr. Julius Butime,  \nDean, School of Computing & Engineering Sciences, Strathmore University  \nDr. Bernard Shibwabo, Director of Graduate Studies, Strathmore University  \nii  \nAbstract  \nCovid-19 fast tracked the transition of ways of working and learning into remote working models and with it the rise of Cyber Crime. Phishing is one of the most effective cybercrimes where attackers deceive users into falling for baits that enable them to steal personal information including identification or social security number, password, credit card details, debit card details and usernames which they then use to commit crimes mostly associated with financial losses and or identity thefts. These attacks are frequently carried out through malicious emails, texts, and phone calls. Even in the presence of robust antivirus software and phishing detection tools, phishing has continued to be rampant and widespread as adoption of IT across the world goes up. Several methods have been implemented to deal with phishing attacks including, blacklisting phishing Uniform Resource Locators (URLs), heuristic rule based and machine learning models. The blacklist anti-phishing approach is limited in its ability to detect new phishing URLs due to its reliance on a pre-compiled list of phishing URLs, whereas most Machine Learning (ML) methods for detecting phishing websites have been reported with very decent detection accuracy but significant false alarm rates. Attackers are continuously re-inventing methods of attacks which as well makes heuristic rule-based methods vulnerable to failed detection. This study has provided further information on phishing attacks to create awareness, designed and implemented a web-based phishing links detection tool using ML deep learning model which achieved an accuracy of 94.27 complemented by heuristic rules and blacklist capabilities. Further improvement areas including continuous models training and features reengineering for improved prediction accuracy, and equipping Internet of Th","cbCaih2jtHsUtwgr","https://ap.wps.com/l/cbCaih2jtHsUtwgr","pdf",3356485,1,119,"English","en",105,"# Chapter 1: Introduction\n## Background\n## Problem Statement\n# Definition of Terms\n# List of Figures\n# List of Tables\n# Code Listings\n# List of Abbreviations\n# Acknowledgements\n# Dedication","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses phishing links that deceive users and enable theft of personal information and financial or identity-related harm.\"},{\"question\":\"How does the proposed solution detect phishing links?\",\"answer\":\"It implements a web-based detection tool using an ML deep learning model, enhanced with heuristic rules and blacklist capabilities.\"},{\"question\":\"What detection performance is reported?\",\"answer\":\"The tool achieves an accuracy of 94.27% based on the study’s implemented approach.\"}]","A Web Application to Detect Phishing Links Using Machine Learning Approach | PDF",1785808994,300,{"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},"a-web-application-to-detect-phishing-links-using-machine-learning-approach","",{"@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/a-web-application-to-detect-phishing-links-using-machine-learning-approach/122136/",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},"What problem does the thesis address?","Question",{"text":75,"@type":76},"It addresses phishing links that deceive users and enable theft of personal information and financial or identity-related harm.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed solution detect phishing links?",{"text":80,"@type":76},"It implements a web-based detection tool using an ML deep learning model, enhanced with heuristic rules and blacklist capabilities.",{"name":82,"@type":73,"acceptedAnswer":83},"What detection performance is reported?",{"text":84,"@type":76},"The tool achieves an accuracy of 94.27% based on the study’s implemented approach.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]