[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120931-en":3,"doc-seo-120931-105":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},120931,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Design of Efficient Phishing Detection Model using Machine Learning","Phishing attacks increasingly target users by impersonating major websites and stealing personal information through deceptive pages. Countermeasures are often deployed only after an attack is identified, motivating a proactive approach based on machine learning. This paper designs a phishing detection model by learning patterns from input data, building an analysis model using sklearn logistic regression, and visualizing phishing probabilities with a heatmap. Results are further communicated via graphs, along with website attribute information for interpretability.","ISSN 1846-6168 (Print), ISSN 1848-5588 (Online) Original scientific paper  \n[https://doi.org/10.31803/tg-20230219213151](https://doi.org/10.31803/tg-20230219213151) Received: 2023-02-19, Accepted: 2023-05-26  \nDesign of Efficient Phishing Detection Model using Machine Learning  \nBong-Hyun Kim  \nAbstract: Recently, there have been cases of phishing attempts to steal personal information through fake sites disguised as major sites. Although phishing attacks continue and increase, countermeasures remain in the form of defense after identifying the attack. Therefore, in this paper, we designed a phishing detection model using machine learning that provides knowledge and prediction by learning patterns from data input to a computer. For this, an analysis model was built using sklearn logistic regression, and the phishing probability was visualized using a heatmap. In addition, a graph was used to visually indicate the result, and a function for attribute information of a phishing website was provided.  \nKeywords: ensemble method; heatmap; machine learning; phishing detection; random forest; sklearn  \n1 INTRODUCTION  \nMany victims occur every year as a result of deceiving others over the phone or the Internet, or stealing identity or financial information through phishing, pharming, and smishing. Until recently, there have been cases of phishing attempts to steal personal information through fake sites disguised as major sites. Although phishing attacks continue and increase, countermeasures remain in the form of defense after identifying the attack [1] . A phishing site refers to a malicious web site that requests personal and financial information from users through a web page similar to the real thing and causes various attacks, particularly financial damage. The attacker composes and sends an attack email or message to the user, convincing the user to connect to a spoofed server [2] . If the page displayed by the spoofed server is mistaken for the real server and personal information is entered, the information is delivered to the attacker who manages the spoofed server. Actual phishing attack methods and routes vary by phone call and text message.  \nIn computing, phishing is the act of using e-mail or messenger to deceive by pretending to be a message from a trusted person or company. This deception is a form of social engineering that attempts to fraudulently obtain confidential information such as passwords and credit card information. As reports of phishing incidents increase, methods to prevent phishing are needed. These methods include law, user training, and technical tools. Recently, in addition to phishing using a computer, phishing using a phone is also called voice phishing. There are many different types of phishing.  \nTo prevent and minimize this damage, we are working to eradicate phishing scams worldwide. Korea stipulates punishment for fraud under the \"Criminal Act\", punishment for telecommunication financial fraud under the \"Special Acton Prevention of Damages from Telecommunication Financial Fraud and Refund of Damages\", and penalties for falsification and false display of phone numbers under the Telecommunications Business Act. Since 2012, a comprehensive government-wide response system has been prepared and operated [3, 5] .  \nThe US federal government has the \"Identity Fraud and Impersonation Prevention Act\" and the \"Identity Fraud Enforcement Punishment Act\" to protect personal  \ninformation. In addition, states such as California, Florida, and Illinois have state-level phishing fraud prevention laws. Currently, the \"Fraud and Scam Prevention Act\" to protect the elderly who are susceptible to fraud has passed the US House of Representatives and is before the Senate [4] .  \nSimilar to Korea’s legal system, Japan is governed by the Act on the Prevention of Illegal Use of Mobile Voice Communication Services and Identification of Contractors by Mobile Voice Communication Operators, and the Payment of Damages Re","cbCaikOiYWm1WIFo","https://ap.wps.com/l/cbCaikOiYWm1WIFo","pdf",1446848,1,6,"English","en",105,"# Introduction\n## Phishing threats and attack routes\n## Definitions and prevention approaches\n## Legal and policy measures\n## Motivation for machine learning and deep learning","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses the growing phishing threat that steals personal and financial information by disguising fake sites as legitimate ones.\"},{\"question\":\"How is the phishing detection model built?\",\"answer\":\"A phishing detection model is designed using machine learning, with an analysis model implemented via sklearn logistic regression.\"},{\"question\":\"How are results visualized and presented?\",\"answer\":\"Phishing probabilities are visualized using a heatmap, and graphs are used to visually indicate the results along with phishing website attribute information.\"}]","Design of Efficient Phishing Detection Model using Machine Learning | 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problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses the growing phishing threat that steals personal and financial information by disguising fake sites as legitimate ones.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the phishing detection model built?",{"text":80,"@type":76},"A phishing detection model is designed using machine learning, with an analysis model implemented via sklearn logistic regression.",{"name":82,"@type":73,"acceptedAnswer":83},"How are results visualized and presented?",{"text":84,"@type":76},"Phishing probabilities are visualized using a heatmap, and graphs are used to visually indicate the results along with phishing website attribute 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