[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123372-en":3,"doc-seo-123372-105":30,"detail-sidebar-cat-0-en-105":92},{"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},123372,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Malicious URL detection using machine learning techniques","With the rapid growth of new websites, distinguishing safe pages from potentially harmful ones becomes increasingly difficult, especially when attackers can harvest sensitive user data without adequate cybersecurity controls. This study develops machine learning models to detect and categorize malicious URLs. Decision trees, logistic regression, support vector machines, and Naive Bayes are evaluated, with grid-search hyperparameter tuning to improve classification efficiency. Results show Naive Bayes reaches 91.9% accuracy and the approach is implemented as a web service for practical integration into security frameworks.","Malicious URL detection using machine learning techniques  \nMohamed Cherradi1, Hajar El Mahajer2  \n1Abdelmalek Essaâdi University (UAE), ENSAH, Tetouan, Morocco  \n2Abdelmalek Essaâdi University (UAE), FSTT, Tetouan, Morocco  \nArticle Info ABSTRACT  \n\n| Article history:\u003Cbr>Received March 16, 2025 Revised May 12, 2025 Accepted May 18, 2025 | With numerous new websites being created every day, it's getting increasingly challenging to tell which ones are safe and which could be dangerous. These websites frequently gather sensitive user data that may be hacked in the absence of proper cybersecurity safeguards, such as the effective identification and categorization of dangerous URLs. In order to improve cybersecurity, this study attempts to create models based on machine learning algorithms for the effective detection and categorization of harmful URLs. In this regard, our proposal uses decision trees, logistic regression, support vector machines, and Naive Bayes to reliably categorize dangerous URLs. To improve classification efficiency, we have integrated hyperparameter tuning using the Grid Search technique, optimizing model performance for more accurate and reliable results. The results demonstrate the effectiveness of Naive Bayes in achieving high accuracy (91.9%) and reliable performance in detecting malicious URLs. Implementation as a web service of the study provides evidence of the practicality and natural fit into more generalized security frameworks. Ultimately, our approach significantly enhances the detection of unsafe URLs, offering a robust solution to address the growing challenges in cybersecurity.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Cybersecurity Malicious URLs Machine Learning Classification |  |\n\nCorresponding Author: Mohamed Cherradi ([e-mail: m.cherradi@uae.ac.ma](e-mail: m.cherradi@uae.ac.ma))  \n1. INTRODUCTION  \nMany new internet pages are made every day that use login features to gather user information. It is difficult to identify which one is trustworthy and safe due to the enormous number of websites [1] . In this situation, cybersecurity plays a crucial role. A collection of methods or instruments designed to defend consumers and businesses against cyberattacks is known as cybersecurity [2] . Malicious URLs, or hyperlinks, are a key tool used by hackers to trick Internet users into divulging private and sensitive information in this huge digital environment. Users who interact with these links put themselves at risk of negative outcomes, such as the compromise of private data or being the focus of cyberattacks.  \nCybercriminals use various techniques to take advantage of human and system vulnerabilities. Phishing is one of the most popular techniques, in which attackers attempt to fool targets into disclosing private information that could have dire repercussions [3] . Another method that attackers employ to alter webpages' content is defacement, which involves altering the source code. This type of cyberattack is commonly used to compromise a company's website [4] . The ways that cybercriminals utilize misleading website addresses to spread and run malware are known as malware techniques in harmful URLs. These methods seek to send malicious payloads, trick users, and take advantage of software flaws. According to a 2013 RSA research [5], phishing attacks caused losses on approximately 450,000 websites. Blacklists made up of known malicious URLs are used to combat such threats. However, because new malicious URLs linked to spam and phishing activities are constantly appearing, their effectiveness is still restricted.  \nIn order to detect both new and existing dangerous URLs, machine learning is crucial [6] . Computers may be taught to interpret data through a process called machine learning, which gives them the ability to forecast or decide on their own. Classification, a subfield of supervised machine learning, is the most widely used method","cbCaikxhyK9XviDW","https://ap.wps.com/l/cbCaikxhyK9XviDW","pdf",713378,1,12,"English","en",105,"# INTRODUCTION\n# LITERATURE REVIEW","[{\"question\":\"Why is malicious URL detection important in real-world cybersecurity?\",\"answer\":\"New websites are created continuously, making it hard to determine which links are trustworthy. Malicious URLs can trick users into revealing sensitive information and lead to cyberattacks or data compromise.\"},{\"question\":\"Which machine learning models are used to detect and categorize malicious URLs?\",\"answer\":\"The study benchmarks four algorithms: Logistic Regression, Support Vector Machine, Naive Bayes, and Decision Tree.\"},{\"question\":\"How does the study improve model performance?\",\"answer\":\"It applies hyperparameter tuning using the Grid Search technique to optimize each model’s performance for more accurate and reliable detection.\"}]","Malicious URL detection using machine learning techniques | PDF",1785816167,30,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"malicious-url-detection-using-machine-learning-techniques","",{"@graph":36,"@context":86},[37,54,69],{"@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/malicious-url-detection-using-machine-learning-techniques/123372/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is malicious URL detection important in real-world cybersecurity?","Question",{"text":76,"@type":77},"New websites are created continuously, making it hard to determine which links are trustworthy. Malicious URLs can trick users into revealing sensitive information and lead to cyberattacks or data compromise.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are used to detect and categorize malicious URLs?",{"text":81,"@type":77},"The study benchmarks four algorithms: Logistic Regression, Support Vector Machine, Naive Bayes, and Decision Tree.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study improve model performance?",{"text":85,"@type":77},"It applies hyperparameter tuning using the Grid Search technique to optimize each model’s performance for more accurate and reliable detection.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"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":107,"slug":138},19,"General","general"]