[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125613-en":3,"doc-seo-125613-105":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":4,"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},125613,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",6,"Technology","Identification of Phishing Attacks using Machine Learning Algorithm - Slideshare","Phishing is a widespread cybercrime in which attackers trick users into exposing sensitive data, and it has evolved into a complex attack vector. It is carried out through emails, phone calls, chats, advertisements, pop-ups, and DNS poisoning, leading to losses such as confidential-data theft, identity fraud, and compromise of organizations. The article develops a detailed model covering attack stages, attacker types, threats, targets, attack media, and strategies. Websites are categorized as legitimate or phishing using machine learning methods including Random Forest, XGBoost, and Logistic Regression, supporting awareness and anti-phishing system design.","Identification of Phishing Attacks using Machine Learning Algorithm  \nDinesh P.M 1􀀍 , Mukesh M 1, Navaneethan B2, Sabeenian R. S1, Paramasivam M.E1, and Manjunathan A3  \n1Department of Electronics and Communication Engineering, Sona College of Technology, Salem, India  \n2Spring Five, Bangalore, India  \n3Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy-621112, Tamil Nadu, India  \nAbstract. Phishing is a particular type of cybercrime that allows criminals to trick people and steal crucial data. The phishing assault has developed into a more complex attack vector since the first instance was published in 1990. Phishing is currently one of the most prevalent types of online fraud behavior. Phishing is done using a number of methods, such as through emails, phone calls, instant chats, adverts, pop-up windows on websites, and DNS poisoning. Phishing attacks can cause their victims to suffer significant losses, including the loss of confidential information, identity theft, businesses, and state secrets. By examining current phishing practises and assessing the state of phishing, this article seeks to assess these attacks. This article offers a fresh, in-depth model of phishing that takes into account attack stages, different types of attackers, threats, targets, attack media, and attacking strategies. Here, we categorise websites as real or phishing websites using machine learning techniques including Random Forest, XGBoost, and Logistic Regression.  \nAdditionally, the proposed anatomy will aid readers in comprehending the lifespan of a phishing attack, raising awareness of these attacks and the strategies employed as well as aiding in the creation of a comprehensive anti-phishing system.  \n1 Introduction  \nDue to the significant increase in internet usage, individuals are increasingly sharing their personal information online. This has led to a rise in incidents where fraudsters gain unauthorized access to personal data and engage in financial fraud. Phishing is a technique used by malicious actors to deceive users by impersonating reputable websites and tricking them into revealing sensitive information like passwords, account details, and credit card numbers. While various anti-phishing tools and methods exist to detect and prevent such attacks in emails and websites, phishers continuously develop new and hybrid methods to evade these defenses. According to research conducted by the Anti-Phishing Working  \n􀀍 Corresponding author: [pmdineshece@live.com](pmdineshece@live.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nGroup, there were 1,220,523 distinct phishing scams reported from January to March 2018, with an increasing number of attacks reported daily. Researchers are constantly working on improving existing models to enhance their accuracy and effectiveness. This article presents a comprehensive model of phishing that considers various aspects, including different stages of attacks, types of attackers, threats, targets, attack media, and strategies. The model utilizes a dataset of phishing URLs collected from an open-source service, and employs machine learning techniques such as Random Forest, XGBoost, and Logistic Regression to classify given URLs as either legitimate (0) or phishing (1) websites.  \nAll of these models were developed using the dataset, and the test dataset was used to assess the models.  \nFig. 1. Typical phishing attack  \n2 Literature survey  \nIn their study, Sami Smadi [et.al](et.al) [3] have proposed a novel approach for identifying digital phishing emails using a dynamic expanding neural network that incorporates reinforcement learning. While there are advanced methods available for detecting phishing attacks, there are still limitations in online de","cbCaidLxzaBDHuSg","https://ap.wps.com/l/cbCaidLxzaBDHuSg","pdf",439917,1,10,"English","en",105,"# Abstract\n# Introduction\n# Literature survey\n# Methodology and Model Design\n## Classification using ML Algorithms\n# Experimental Evaluation","[{\"question\":\"What is the main goal of the proposed work on phishing detection?\",\"answer\":\"The work aims to build an in-depth phishing model that considers attack stages, attacker types, threats, targets, attack media, and strategies while classifying URLs as legitimate or phishing using machine learning.\"},{\"question\":\"Which machine learning algorithms are used to classify websites?\",\"answer\":\"The document uses Random Forest, XGBoost, and Logistic Regression to categorize websites as legitimate (0) or phishing (1).\"},{\"question\":\"How does the article describe the impact of phishing attacks?\",\"answer\":\"Phishing attacks can cause victims to lose confidential information, suffer identity theft, and enable financial fraud affecting individuals, businesses, and even state-level secrets.\"}]","Identification of Phishing Attacks using Machine Learning Algorithm - Slideshare | PDF",1785900228,25,{"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},"identification-of-phishing-attacks-using-machine-learning-algorithm-slideshare","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-phishing-attacks-using-machine-learning-algorithm-slideshare/125613/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed work on phishing detection?","Question",{"text":75,"@type":76},"The work aims to build an in-depth phishing model that considers attack stages, attacker types, threats, targets, attack media, and strategies while classifying URLs as legitimate or phishing using machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used to classify websites?",{"text":80,"@type":76},"The document uses Random Forest, XGBoost, and Logistic Regression to categorize websites as legitimate (0) or phishing (1).",{"name":82,"@type":73,"acceptedAnswer":83},"How does the article describe the impact of phishing attacks?",{"text":84,"@type":76},"Phishing attacks can cause victims to lose confidential information, suffer identity theft, and enable financial fraud affecting individuals, businesses, and even state-level secrets.","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},[93,97,101,105,110,113,118,123,128,131,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]