[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120845-en":3,"doc-seo-120845-105":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":11,"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},120845,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Improving Phishing Website Detection with Machine Learning - Revealing Hidden Patterns for Better Accuracy","Phishing attacks remain a major threat to internet users worldwide, causing financial losses and exposing personal information. This research evaluates multiple machine learning models for phishing website detection, prioritizing high accuracy. Random Forest is selected after extensive analysis, leveraging subtle data patterns beyond traditional URL and content restrictions. Using URL and derived attributes, page source code, HTML/JavaScript features, and domain-based features, the system classifies most websites with accuracy over 98%, recall above 98%, and false positives under 4%.","Improving Phishing Website Detection with Machine Learning: Revealing Hidden Patterns for Better  \nAccuracy  \nGarlapati Narayana*1, Uma Devi Manchala2, Usikela Naresh3, Saggurthi Kiran4, Medikonda Asha Kiran5, Ravi Kumar  \nCh6  \n1Associate Professor, Department ofCSE (AIML),  \nChaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India  \n[narayanag.1973@gmail.com](narayanag.1973@gmail.com)  \nORCID: [https://orcid.org/0000-0001-8470-3595](https://orcid.org/0000-0001-8470-3595)  \n2Assistant Professor. Department of Computer Science and Engineering,  \nNalla Narsimha Reddy Education society's Group of Institutions, Chowdariguda, Medchal, Telangana, India  \n[umadevi.manchala92@gmail.com](umadevi.manchala92@gmail.com)  \nORCID: [https://orcid.org/0000-0002-8325-3868](https://orcid.org/0000-0002-8325-3868)  \n3Assistant Professor. Department of Computer Science and Engineering (AI&ML),  \nCVR College of Engineering, Mangalpalli, Rangareddy, Telangana, India  \n[usikelanaresh@gmail.com](usikelanaresh@gmail.com)  \nORCID: [https://orcid.org/0009-0006-9656-4880](https://orcid.org/0009-0006-9656-4880)  \n4Assistant Professor, Department of Computer Science and Engineering (AI&ML),  \nCMR Technical Campus, kandlakoya, Medchal, Telangana, India  \n[kiransaggurthicfc@gmail.com](kiransaggurthicfc@gmail.com)  \nORCID: [https://orcid.org/0009-0002-5997-2288](https://orcid.org/0009-0002-5997-2288)  \n5Assistant Professor, Department of AIML,  \nChaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India  \n[ashakiran2@gmail.com](ashakiran2@gmail.com)  \nORCID: [https://orcid.org/0000-0002-7760-2902](https://orcid.org/0000-0002-7760-2902)  \n6Assistant Professor, Department of AI&DS,  \nChaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India  \n[chrk5814@gmail.com](chrk5814@gmail.com)  \nORCID: [https://orcid.org/0000-0003-0809-5545](https://orcid.org/0000-0003-0809-5545)  \nAbstract: Phishing attacks remain a significant threat to internet users globally, leading to substantial financial losses and compromising personal information. This research study investigates various machine learning models for detecting phishing websites, with a primary focus on achieving high accuracy. After an extensive analysis, the Random Forest Classifier emerged as the most suitable choice for this task. Our methodology leveraged machine learning techniques to uncover subtle patterns and relationships in the data, going beyond traditional URL and content-based restrictions. By incorporating diverse website features, including URL and derived attributes, Page source code-based features, HTML JavaScript-based features, and Domain-based features, we achieved impressive results. The proposed approach effectively classified the majority of websites, demonstrating the efficiency of machine learning in addressing the phishing website detection challenge with an accuracy of over 98%, recall exceeding 98%, and a false positive rate of less than 4% . This research offers valuable insights to the field of cyber security, providing internet users with improved protection against phishing attempts.  \nKeywords: Phishing attacks, accuracy, machine learning model, optimal parameters, Cyber security.  \nI. INTRODUCTION  \nThe internet has revolutionized the way we conduct business, communicate, and access information. However, this digital transformation has brought about a dark side: cybercrime. Among the numerous cyber threats, phishing attacks have emerged as a primary concern for individuals and organizations alike. Phishes employ social engineering  \ntechniques to manipulate human vulnerability, luring unsuspecting victims into revealing sensitive information or performing actions that can have dire consequences [1][2] .  \nPhishing attacks typically involve the distribution of deceptive emails or messages containing fraudulent links. Once recipients fall into the trap, cybercriminals exploit this opportunity to gain unauthorize","cbCaifDRFFxt4q7R","https://ap.wps.com/l/cbCaifDRFFxt4q7R","pdf",295364,1,"English","en",105,"# Introduction\n## Challenges with Traditional Methods\n## The Machine Learning-Based Approach","[{\"question\":\"Which machine learning model performed best for phishing website detection?\",\"answer\":\"Random Forest Classifier emerged as the most suitable choice after extensive analysis and model comparison.\"},{\"question\":\"What types of features were used to detect phishing websites?\",\"answer\":\"The approach used URL and derived attributes, page source code features, HTML/JavaScript-based features, and domain-based features.\"},{\"question\":\"What accuracy-related performance results does the proposed method achieve?\",\"answer\":\"The approach reports accuracy over 98%, recall exceeding 98%, and a false positive rate less than 4%.\"}]","Improving Phishing Website Detection with Machine Learning - Revealing Hidden Patterns for Better Accuracy | PDF",1785732312,20,{"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},"improving-phishing-website-detection-with-machine-learning-revealing-hidden-patterns-for-better-accuracy","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"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":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/improving-phishing-website-detection-with-machine-learning-revealing-hidden-patterns-for-better-accuracy/120845/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",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},"Which machine learning model performed best for phishing website detection?","Question",{"text":74,"@type":75},"Random Forest Classifier emerged as the most suitable choice after extensive analysis and model comparison.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What types of features were used to detect phishing websites?",{"text":79,"@type":75},"The approach used URL and derived attributes, page source code features, HTML/JavaScript-based features, and domain-based features.",{"name":81,"@type":72,"acceptedAnswer":82},"What accuracy-related performance results does the proposed method achieve?",{"text":83,"@type":75},"The approach reports accuracy over 98%, recall exceeding 98%, and a false positive rate less than 4%.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]