[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122870-en":3,"doc-seo-122870-105":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},122870,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Real-Time Detection of Phishing Emails Using XGBoost Machine Learning Technique","Phishing attacks remain a major threat to individuals and organizations, requiring more effective countermeasures. This study compares four machine learning models for phishing email detection—Random Forest, Decision Tree, XGBoost, and Logistic Regression—using a labeled dataset. Performance is evaluated with accuracy, precision, and recall, alongside considerations such as training time, test time, model size, interpretability, and explainability. Results show XGBoost achieves the highest accuracy and strong precision, supporting practical development of antiphishing technologies for protecting sensitive personal data.","Real-Time Detection of Phishing Emails Using XG Boost Machine Learning Technique  \nJude Osamor*  \nDepartment of Cybersecurity and Networks Glasgow Caledonian University Glasgow, G4 0BA Scotland, UK  \n[jude.osamor@gcu.ac.uk](jude.osamor@gcu.ac.uk)  \nPius Owoh  \nDepartment of Cybersecurity and Networks Glasgow Caledonian University Glasgow, G4 0BA Scotland, UK  \n[nsikak.owoh@gcu.ac.uk](nsikak.owoh@gcu.ac.uk)  \nMoses Ashawa  \nDepartment of Cybersecurity and Networks Glasgow Caledonian University Glasgow, G4 0BA Scotland, UK  \n[moses.ashawa@gcu.ac.uk](moses.ashawa@gcu.ac.uk)  \nAyodeji Ajibade  \nDepartment of Cybersecurity and Networks Glasgow Caledonian University Glasgow, G4 0BA Scotland, UK  \n[aajiba300@caledonian.ac.uk](aajiba300@caledonian.ac.uk)  \nJackie Riley  \nDepartment of Cybersecurity and Networks Glasgow Caledonian University Glasgow, G4 0BA Scotland, UK  \n[j.riley@gcu.ac.uk](j.riley@gcu.ac.uk)  \nCelestine Iwendi  \nSchool of Creative Technologies University of Bolton Bolton, BL3 5AB UK  \n[c.iwendi@bolton.ac.uk](c.iwendi@bolton.ac.uk)  \nAbstract— Phishing attacks continue to pose a significant threat to individuals and organizations, making it crucial to develop effective countermeasures. Machine learning algorithms have shown promise in detecting and mitigating phishing attacks. The study evaluates the performance of four popular algorithms in the context of phishing detection and compares the effectiveness of these four different algorithms; Random Forest, Decision Tree, XGBoost, and Logistic Regression, to determine which one achieves the highest accuracy. The results show that XGBoost outperforms the other algorithms and can accurately detect phishing attacks with a high degree of precision. The algorithms are compared based on factors such as training time, test time, model size, interpretability, and explainability. To compare the effectiveness of these algorithms, the study conducted experiments using a dataset of phishing emails. The algorithms were trained on a labeled dataset and evaluated based on metrics such as accuracy, precision, and recall. The results demonstrate that XGBoost outperforms the other algorithms, achieving the highest accuracy in detecting phishing attacks. The findings of this study have significant implications for the development of antiphishing technologies. By leveraging machine learning algorithms, particularly XGBoost, organizations can enhance their ability to detect and prevent phishing attacks. This can help protect individuals' personal information, passwords, and credit card numbers from falling into the hands of cybercriminals.  \nKeywords—Phishing, XGBoost, email security,cybersecurity  \nI. INTRODUCTION  \nPhishing attacks have become a significant cybersecurity threat, posing severe risks to individuals, organizations, and governments. These attacks involve tricking individuals into providing sensitive information such as passwords, credit card numbers, or social security numbers by impersonating a trusted entity. With the increasing sophistication of phishing techniques, it has become more challenging to detect and prevent these attacks, making it crucial for individuals and organizations to stay vigilant and adopt proactive measures to protect themselves from falling victim to such scams. phishing attacks are considered one of the most frequent examples of fraud activity on the internet [1] .  \nSome common types of phishing attacks include email phishing, where attackers send deceptive emails to trick recipients into revealing personal information or clicking on malicious links. Another form is spear phishing, which targets specific individuals or organizations with personalized and highly convincing messages. There is also vishing, a phishing technique that involves phone calls or voice messages to deceive victims into sharing sensitive data. Additionally, thereis smishing, where attackers use SMS or text messages to trick recipients into providing personal information or dow","cbCaiaAkT4U2FGo3","https://ap.wps.com/l/cbCaiaAkT4U2FGo3","pdf",1113907,1,16,"English","en",105,"# Introduction\n## Types of phishing attacks\n## Need for real-time detection\n# Methodology and Algorithm Comparison\n## Dataset and labeling\n## Evaluation metrics\n# Results and Discussion\n## Accuracy, precision, recall comparison\n## Training/test efficiency and model traits\n# Implications for Anti-Phishing Technologies","[{\"question\":\"Which machine learning algorithms are evaluated for phishing email detection?\",\"answer\":\"The study evaluates Random Forest, Decision Tree, XGBoost, and Logistic Regression using a labeled phishing email dataset.\"},{\"question\":\"How are the algorithms compared in the study?\",\"answer\":\"Algorithms are compared using accuracy, precision, and recall, and also by factors such as training time, test time, model size, interpretability, and explainability.\"},{\"question\":\"What is the main finding about XGBoost’s performance?\",\"answer\":\"XGBoost outperforms the other algorithms and delivers the highest accuracy, with strong precision for detecting phishing attacks.\"}]","Real-Time Detection of Phishing Emails Using XGBoost Machine Learning Technique | 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machine learning algorithms are evaluated for phishing email detection?","Question",{"text":75,"@type":76},"The study evaluates Random Forest, Decision Tree, XGBoost, and Logistic Regression using a labeled phishing email dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the algorithms compared in the study?",{"text":80,"@type":76},"Algorithms are compared using accuracy, precision, and recall, and also by factors such as training time, test time, model size, interpretability, and explainability.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main finding about XGBoost’s performance?",{"text":84,"@type":76},"XGBoost outperforms the other algorithms and delivers the highest accuracy, with strong precision for detecting phishing 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