[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126149-en":3,"doc-seo-126149-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126149,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","An Efficient Machine Learning Based Approach For Phishing Detection","Phishing presents a serious breach of information safety, enabling attackers to obtain sensitive personal credentials by using counterfeit web links that closely mimic legitimate sites. These attacks often begin with fraudulent emails or crafted communications designed to deceive victims into clicking provided URLs. Incorrectly trusting malicious links can expose user information, allow misuse of data, and enable credential theft. The approach summarizes a machine-learning workflow using libraries to classify URLs as phishing or legitimate, aiming to detect attacks effectively.","An Efficient Machine Learning Based Approach For  \nPhishing Detection  \n1Mohamed Abdelshafea Mousa Abbas, 1Ruth Ramya,1 P.Vidyullatha, 2M Suman, 2Syed Inthiyaz  \n1Department ofCSE, Koneru Lakshmaiah Education Foundation, Guntur, AP, India-522302  \n2Department of ECE, Koneru Lakshmaiah Education Foundation, Guntur, AP, India-522302  \nAbstract—Phishing is a breach of statistics safety through which attackers can advantage get admission to sensitive individual credentials through manner of using counterfeit net web sites closely equal to legitimate net web sites. Phishing starts of evolved with a fraudulent emails or exceptional communique that is designed to attack on a victim. If the victim clicks on immediately to the given url through manner of the cyber-attack that the attacker can get the extraordinary statistics or the essential statistics of the patients and misuse the statistics. There are one in all a type sorts of set of guidelines that can be used to come across the given url , whether or not or now no longer it is good url or the awful url . Among the ones all of these algorithms some algorithms will the ideal stop end result or the maximum percentage of the phishing attack detector. Some of the algorithms with a view to supply the almost accurate outcomes are, Random Forest Algorithm, Decision Tree Algorithm. The message exactly seems like the precise message which have become sent from the attackers but appears exactly similar to the message from an authorized enterprise agency or a company. This assignment can be accomplished through manner of using the Machine Learning using some libraries.  \nKeywords-Phishing; Personal Information; Machine Learning; vicious Links.  \nI. INTRODUCTION  \nPhishing Attacks has have come a large trouble now a days, that the sufferers were given without problems trapped with inside the arms of the attackers which became very unlawful and unhappy aspect for the sufferer. The essential factor that must betaken into consideration is that the customers should or capable of apprehend that which URL is malicious and which URL is secure to apply and may use the hyperlink. So, to apprehend the trouble and act accordingly, this mission will assist the customers to become aware of the URL that is secure or now no longer. To resolve the trouble, in this mission we've evolved a software program which detects that the given url is phishing URL or it's far a now no longer a phishing URL. For this we've evolved a software program the use of the Machine Learning with a number of the libraries with inside the python. The fraud internet site which appears precisely because the unique URL internet site. Experts can become aware of faux web sites however now no longer all the customers can become aware of the faux internet site in the end the person can turn out to be the sufferer of the cyber assault through the attackers and lead those to reachable to the customer’s non-public facts and the exclusive facts. Also, the attacker can be capable of souse borrow banks account credentials. Phishing assaults made smooth for the attackers because of loss of person awareness.  \nII. LITERATURE REVIEW  \nIn [4] the Author have detected phishing web sites through  \nthe usage of diverse device gaining knowledge of algorithms after which as compared the accuracy of the one-of-a-kind algorithms. Their experimental effects indicated at Random Forest set of rules having the very best accuracy, don't forget and precision. A class version is proposed in to categorize the phishing assaults. Feature extraction turned into completed from diverse web sites primarily based totally at the UCI Irvine ML  \nrepository. Authors of proven a technique of detecting phishing e mail assaults the usage of NLP and ML. In [2] they executed semantic evaluation of textual content for detecting any type of malicious activity. NLP turned into used to parse sentences the authors have proposed algorithms for function choice for phishing detection t","cbCaicuY2kA4YUcj","https://ap.wps.com/l/cbCaicuY2kA4YUcj","pdf",296458,9,1,7,"English","en",105,"# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"What is the main goal of the proposed phishing detection approach?\",\"answer\":\"To classify a given URL as phishing or non-phishing, helping users identify malicious links before they are used in an attack.\"},{\"question\":\"How do phishing attacks typically work?\",\"answer\":\"They use fraudulent emails or crafted messages to trick victims into clicking links that lead to counterfeit sites resembling legitimate ones.\"},{\"question\":\"Which machine learning algorithms are highlighted for phishing detection?\",\"answer\":\"Random Forest and Decision Tree are mentioned as providing effective results, along with references to other models in the literature.\"}]","An Efficient Machine Learning Based Approach For Phishing Detection | PDF",1785903409,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"an-efficient-machine-learning-based-approach-for-phishing-detection","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/an-efficient-machine-learning-based-approach-for-phishing-detection/126149/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of the proposed phishing detection approach?","Question",{"text":77,"@type":78},"To classify a given URL as phishing or non-phishing, helping users identify malicious links before they are used in an attack.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How do phishing attacks typically work?",{"text":82,"@type":78},"They use fraudulent emails or crafted messages to trick victims into clicking links that lead to counterfeit sites resembling legitimate ones.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning algorithms are highlighted for phishing detection?",{"text":86,"@type":78},"Random Forest and Decision Tree are mentioned as providing effective results, along with references to other models in the literature.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]