[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122532-en":3,"doc-seo-122532-105":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},122532,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine Learning Classifier Performance Comparison for Phishing Detection - Slide","Rapid advances in technology have made email a convenient channel for communication, but the same medium enables abuse through spam, viruses, advertisements, and phishing. Phishing aims to steal sensitive data by sending fraudulent emails or directing victims to deceptive websites and payment pages. This study compares multiple machine learning algorithms for phishing/spam identification using various performance criteria, then contrasts model outcomes to assess effectiveness and support improved detection decisions.","Machine Learning Classifier Performance Comparison for Phishing Detection  \nIqbal Hadi Azmi  \nFaculty Computer Science and Information Technology Universiti Putra Malaysia Malaysia [gs61356@student.upm.edu.my](gs61356@student.upm.edu.my)  \nNor Fazlida Mohd Sani  \nFaculty Computer Science and Information Technology Universiti Putra Malaysia Malaysia  \nAbstract—Technology has advanced at a remarkable rate in recent decades, making communication simpler. Emails are the most effective method for both casual and formal conversations when compared to other forms of communication. Emails are a common form of communication for both business and personal purposes. Unfortunately, emails are also used to annoy internet users by sending viruses, spam, and ads. Spam emails are those sent by some undesirable users, also referred to as spammers. Some individuals misuse this kind of communication by sending spam emails that contain links to specific URLs for users to click on. Spam generates a number of issues, some of which may result in financial losses. Phishing is a method for attempting to obtain sensitive data through fraudulent email or website solicitation. This study compares the effectiveness of different machine learning algorithms in identifying spam or phishing emails. Different performance criteria were taken into account when evaluating the models, and the outcomes were compared.  \nKeywords—Phishing Detection, Spam email, Machine Learning Techniques  \nI. INTRODUCTION  \nSpam is any irrelevant and undesired communication or unwanted email that is sent by the attacker via email or another information-sharing medium to a significant number of recipients[1] . Spam causes unnecessary use of server resources since there are so many unsolicited emails that need to be processed. Over 77% of all email traffic worldwide is spam, which poses an increasing threat on an annual basis. Users who get spam emails find it annoying. Internet scams and other dishonest tactics used by spammers to trick users into disclosing sensitive personal information have affected many users. According to statistics, spam emails made up 56.87% of all email traffic worldwide, with dating and healthcare spam being the most common types[2] .  \nPhishing incidents are the most frequent attacks carried out by social engineers in recent years. Through phone calls or emails, they seek to deceptively get private and personal information from their intended targets. Attackers deceive victims in order to get private and sensitive information. They involve clicking on a link included in the emails, visiting fake websites, emails, PayPal websites, and so on. Phishing attacks are the most frequent attacks executed by social engineers [3-  \n4] . They aim to deceitfully get private and secret information from intended targets via phone calls or emails [5] . Attackers trick their victims into giving them access to private and sensitive data, including credit card numbers and any other information that can be used to log into accounts with high security, including online banking or services [6] .  \nSpam emails are now more prevalent for a variety of purposes, including advertising, multi-level marketing, chain letters, political correspondence, stock market advice, and so forth. Since machine learning algorithms can adapt to different situations, they have the potential to learn and recognize phishing messages. Based on what the machine has learned, new rules were developed and used during the spam filtering process.  \nThe spam filtering decisions were updated in light of the contents using these dynamic approaches for recognizing the contents of the emails. Machine learning algorithms frequently utilize content-based filtering to provide automated filtering rules and categorize emails. In accordance with [7], the frequency and distribution of terms and phrases in email content were examined. The developed rules were used to filter incoming email spam. Utilizing an adaptive spam ","cbCaigJzYJPun7nv","https://ap.wps.com/l/cbCaigJzYJPun7nv","pdf",1393501,1,6,"English","en",105,"# Introduction\n## Spam filtering techniques\n# Related Works\n## Spam filtering techniques","[{\"question\":\"What problem does the document address?\",\"answer\":\"The document addresses phishing and spam emails, which can trick users into revealing sensitive information and cause security and financial risks.\"},{\"question\":\"How does the study evaluate machine learning models?\",\"answer\":\"It compares different machine learning algorithms using multiple performance criteria when identifying spam or phishing emails, then contrasts the results.\"},{\"question\":\"Which machine learning approaches are mentioned in related work?\",\"answer\":\"The document cites several approaches for spam filtering, including Bayesian Naive classification, K Nearest Neighbour, Neural Networks, and Support Vector Machine.\"}]","Machine Learning Classifier Performance Comparison for Phishing Detection - Slide | PDF",1785811123,15,{"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},"machine-learning-classifier-performance-comparison-for-phishing-detection-slide","",{"@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/machine-learning-classifier-performance-comparison-for-phishing-detection-slide/122532/",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","2026-08-04",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 document address?","Question",{"text":75,"@type":76},"The document addresses phishing and spam emails, which can trick users into revealing sensitive information and cause security and financial risks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study evaluate machine learning models?",{"text":80,"@type":76},"It compares different machine learning algorithms using multiple performance criteria when identifying spam or phishing emails, then contrasts the results.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are mentioned in related work?",{"text":84,"@type":76},"The document cites several approaches for spam filtering, including Bayesian Naive classification, K Nearest Neighbour, Neural Networks, and Support Vector Machine.","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"]