[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118272-en":3,"doc-seo-118272-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118272,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","PhishGuard - Machine Learning-Powered Phishing URL Detection","Phishing is a major internet security threat that targets users’ human weaknesses rather than software flaws by directing them to malicious websites for theft of sensitive information. Existing phishing-URL detection approaches often suffer from limited accuracy and high false-positive rates. This work proposes a machine-learning model for phishing URL detection using a dataset of 500K+ entries from Kaggle and trains five supervised methods (KNN, LR, DT, SVM, RF). Performance is evaluated with accuracy, precision, and recall, where Logistic Regression delivers the strongest results.","2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) | 979-8-3503-2759-5/23/$31.00 ©2023 IEEE | DOI: 10. 1 109/CSCE60160. 2023.00371  \n2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)  \nPhishGuard: Machine Learning-Powered Phishing  \nURL Detection  \nSaydul Akbar Murad 1 , Nick Rahimi 1 *, and Abu Jafar Md Muzahid2  \n1 School of Computing Sciences & Computer Engineering, University of Southern Mississippi,  \nHattiesburg, USA  \n2 Faculty of Computing, University Malaysia Pahang, Pahang, Malaysia  \nE-mail: [saydulakbar.murad@usm.edu](saydulakbar.murad@usm.edu), [nick.rahimi@usm.edu](nick.rahimi@usm.edu), [mrumi98@gmail.com](mrumi98@gmail.com)  \nAbstract—Phishing is a major threat to internet security, targeting human vulnerabilities instead of software vulnerabilities. It involves directing users to malicious websites where their sensitive information can be stolen. Many researchers have worked on detecting phishing URLs, but their models have limitations such as low accuracy and high false positives. To address these issues, we propose a machine-learning model to detect phishing URLs. To detect these malicious URLs, we use a dataset of over 500K entries collected from the Kaggle website. The dataset is used to train five supervised machine-learning techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The aim is to improve the performance of the classifier by studying the features of phishing websites and selecting abetter combination of them. To measure the performance, we considered three parameters: accuracy, precision, and recall. The LR technique yielded the best performance, demonstrating its efficacy in detecting phishing URLs.  \nIndex Terms—Phishing URL, Machine Learning, KNN, SVM, Logistic Regression.  \nI. INTRODUCTION  \nPhishing is a form of online deception where fraudsters fabricate fraudulent websites or emails that seem genuine to dupe users into divulging sensitive information, such as credit card details or login credentials [1] . In recent times, phishing has emerged as one of the most prominent cybersecurity menaces. Phishing attacks can be very effective because they exploit human vulnerabilities rather than technical vulnerabilities. These attacks often use social engineering techniques to trick users into giving away sensitive information, such as login credentials and personal information. Phishing URL detection refers to the process of identifying and blocking URLs (Uniform Resource Locators) that lead to phishing websites [2] . This involves using various techniques to analyze URLs and their associated web content to determine whether they are legitimate or malicious.  \nAs a result, many organizations and individuals have become more aware of the dangers of phishing and have taken steps to protect themselves. One of the most effective ways to prevent phishing attacks is to detect and block phishing URLs. Phishing URL detection research has been ongoing for several years, and it has become increasingly important as phishing attacks have become more sophisticated. Researchers have developed various techniques and algorithms to identify phishing URLs based on different characteristics, such as the  \nURL structure, domain reputation, and content analysis. Some common techniques used in phishing URL detection include:  \n• Blacklisting: This involves maintaining a list of known phishing URLs and blocking access to them.  \n• Machine learning: This involves training models to identify patterns in phishing URLs and using those models to detect new phishing URLs.  \n• URL analysis: This involves analyzing the structure and content of URLs to identify suspicious or malicious characteristics.  \n• Domain reputation analysis: This involves analyzing the reputation of the domain associated with a URL to determine whether it is likely to be malicious.  \n• Content-bas","cbCaib4iIlR81634","https://ap.wps.com/l/cbCaib4iIlR81634","pdf",77116,1,"English","en",105,"# Introduction\n## Phishing and phishing URL detection\n## Common detection techniques and limitations\n# Proposed machine-learning approach\n## Dataset and supervised models\n## Evaluation metrics and results","[{\"question\":\"What is the core problem PhishGuard addresses?\",\"answer\":\"PhishGuard targets phishing URL detection, aiming to identify and block URLs that lead users to phishing websites.\"},{\"question\":\"Which machine-learning models are used in the study?\",\"answer\":\"The study trains five supervised techniques: K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF).\"},{\"question\":\"How is model performance measured and which method performs best?\",\"answer\":\"Performance is measured using accuracy, precision, and recall. Logistic Regression (LR) achieves the best overall performance for detecting phishing URLs.\"}]","PhishGuard - Machine Learning-Powered Phishing URL Detection | PDF",1785682745,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"phishguard-machine-learning-powered-phishing-url-detection","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/technology/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/phishguard-machine-learning-powered-phishing-url-detection/118272/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the core problem PhishGuard addresses?","Question",{"text":74,"@type":75},"PhishGuard targets phishing URL detection, aiming to identify and block URLs that lead users to phishing websites.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine-learning models are used in the study?",{"text":79,"@type":75},"The study trains five supervised techniques: K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF).",{"name":81,"@type":72,"acceptedAnswer":82},"How is model performance measured and which method performs best?",{"text":83,"@type":75},"Performance is measured using accuracy, precision, and recall. 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