[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120933-en":3,"doc-seo-120933-105":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},120933,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Recognizing phishing site using Machine Learning - A Comparative Approach using MultinomialNB & Logistic Regression","Phishing is a technique used to steal sensitive information such as login credentials and credit card details through deceptive emails or websites. Phishing sites are crafted to mislead users into believing they are interacting with legitimate resources, while attackers invest effort to make the page look authentic and the differences hard to notice. This paper presents a machine-learning based method to detect malicious, blocked URLs. The study applies Multinomial Naive Bayes and Logistic Regression, using distinct data collection and text preprocessing steps to improve precision and accuracy, and delivers an application as the final outcome.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-6 Year 2023 Page 955:960  \nRecognizing phishing site using Machine Learning-A Comparative Approach using MultinomialNB & Logistic Regression  \nSupreeth S., Abhishek Nigam, Akansh Srivastava, Aniket Singh, Ashish Kumar Behera  \nSchool of Computer Science and Engineering, REVA University, Bengaluru  \n[supreeth.s@reva.edu.in](supreeth.s@reva.edu.in)  \n[abhiaryan972@gmail.com](abhiaryan972@gmail.com)  \n[akanshsri20@gmail.com](akanshsri20@gmail.com)  \n[aniketsingh.578@gmail.com](aniketsingh.578@gmail.com)  \n*Corresponding author’s E-mail: [supreeth.s@reva.edu.in](supreeth.s@reva.edu.in)  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 30 Nov 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Phishing is a method of trying to collect personal information like login credentials or credit card information using deceptive e-mails or websites. Phishing sites are made to hoodwink clueless clients into intuition they are on an authentic site. The lawbreakers will invest a great deal of energy causing the site to appear as valid as could really be expected and numerous locales will show up practically undefined from the genuine article. This paper proposes a methodology to detect boycotted URLs using machine learning algorithms so that people can be frightened while examining or getting to a particular site. In this project we have using machine learning algorithms such MultinomialNBand Logistic Regression. We used distinctive data and text pre-processing techniques to improve precision and accuracy. An app is developed as the end product of this research work.\u003Cbr>Keywords: Phishing, URLs, Machine Learning, NLP |\n| --- | --- |\n\n1. Introduction  \nPhishing can be depicted as replicating a generous site to swindle clients by using their real factors containing usernames, passwords[15], accounts numbers, public security numbers, and so forth Phishing cheats may be the broadest cybercrime applied today. There are inestimable spaces where phishing assaults can happen like online segment place, webmail, and monetary reason, record working with or cloud limit[16], and different others. The webmail and online segment district had been tormented through phishing more than in some novel industry region. Phishing ought to be appropriate through URL phishing tricks and lance phishing from now on clients need to recall the results and should at this point don't pass on their 100% trust in standard affirmation applications. Machine learning and NLP are one of the productive strategies to choose to phish as it disposes of risks of existing methods. The destination that's the most vital factor within the proposed challenge is to affirm the legitimacy of the web page via catching boycotted URLs. to tell the patron on access boycotted websites through spring up whilst they are trying to get right of entry to and to tell the customer on boycotted websites thru URLs whilst they're attempting to get to. This proposed task will allow the director to feature boycotted URLs to equipped customers all through their request. Right now, individuals achieve maximum online business, transferring cash, charge instalments for instance all of the matters are completed making use of sites or applications. In this way, coming across web page phishing is a huge big thing in our regular existence. It is probably the maximum seasoned type of cyber-attacks, going back to the 90s, it is as but one of the most across-the-board and malicious, with phishing messages and approaches getting regularly superior. Assailants commit notably extra power to deceiving those casualties, who have been selected because the potential prizes are very excessive.  \nI. Literature survey  \nThe emerging headway industry which fundamentally impacts the current security issues has given anon-ease of the brain to some business and home clients. Events that abuse human inadequacies have been on the upsurge","cbCainjvQBKrBnFi","https://ap.wps.com/l/cbCainjvQBKrBnFi","pdf",404019,1,6,"English","en",105,"# Introduction\n# Literature survey","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses detecting phishing sites by identifying blocked or malicious URLs so users can be warned during access.\"},{\"question\":\"Which machine learning methods are used for phishing URL detection?\",\"answer\":\"It uses Multinomial Naive Bayes and Logistic Regression, supported by distinct data and text preprocessing to improve results.\"},{\"question\":\"What is the final deliverable of the research work?\",\"answer\":\"An application is developed as the end product, based on the proposed phishing detection methodology.\"}]","Recognizing phishing site using Machine Learning - A Comparative Approach using MultinomialNB & Logistic Regression | PDF",1785732791,15,{"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},"recognizing-phishing-site-using-machine-learning-a-comparative-approach-using-multinomialnb-logistic-regression","",{"@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/recognizing-phishing-site-using-machine-learning-a-comparative-approach-using-multinomialnb-logistic-regression/120933/",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","2026-08-03",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 paper address?","Question",{"text":75,"@type":76},"The paper addresses detecting phishing sites by identifying blocked or malicious URLs so users can be warned during access.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used for phishing URL detection?",{"text":80,"@type":76},"It uses Multinomial Naive Bayes and Logistic Regression, supported by distinct data and text preprocessing to improve results.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the final deliverable of the research work?",{"text":84,"@type":76},"An application is developed as the end product, based on the proposed phishing detection methodology.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]