[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119223-en":3,"doc-seo-119223-105":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},119223,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Tree-based Ensemble Machine Learning for Phishing Website Detection - Feature Research","Phishing websites pose a major cybersecurity threat by mimicking legitimate sites and tricking users into disclosing sensitive information. As phishing techniques become more sophisticated, traditional detection approaches struggle to keep pace, making accurate and efficient methods essential for impact mitigation. This research evaluates feature selection strategies and tree-based ensemble machine learning models, focusing on Random Forest and Extra Trees, trained and tested on phishing and legitimate website data. Performance is assessed using precision, recall, and accuracy.","Komputika: Jurnal Sistem Komputer  \nVolume 13, Nomor 2, Oktober 2024, hlm. 233-243 DOI: 10.34010/komputika.v13i2 .12495  \np-ISSN: 2252-9039  \ne-ISSN: 2655-3198  \nTree-based Ensemble Machine Learning for Phishing Website Detection  \nHusni Fadhilah1*, Diky Restu Maulana2, Rahayu Utari3  \n1,2,3)Sekolah Teknik Elektro dan Informatika, Institut Teknologi Bandung  \nJl. Ganesha 10, Bandung, Indonesia 40132  \n*[email: 23523034@std.stei.itb.ac.id](email: 23523034@std.stei.itb.ac.id)  \n(Naskah masuk: 11 Maret 2024; direvisi: 12 Oktober 2024; diterima untuk diterbitkan: 26 Oktober 2024)  \nABSTRACT – Phishing websites are a major cybersecurity threat, deceiving users into revealing sensitive information by imitating legitimate websites. As these attacks evolve, accurate and efficient detection methods are crucial for mitigating their impact. Detecting phishing websites using traditional methods has limitations due to the increasing sophistication of phishing techniques. This research addresses the need for more advanced machine learning approaches to improve detection accuracy. The goal of this research is to explore the effectiveness of featureselection techniques and tree-based ensemble machine learning models, specifically Random Forest and Extra Trees, in identifying phishing websites. A comprehensive evaluation of the Random Forest and Extra Trees models was conducted. Feature selection techniques were applied to optimize the detection process, reducing complexity while maintaining high accuracy. The models were trained and tested on a dataset of phishing and legitimate websites, with performance metrics such as precision, recall, and accuracy being analyzed. The findings demonstrate that the Random Forest and Extra Trees models achieved an accuracy of 98.2%, outperforming other machine learning approaches in detecting phishing websites. These results suggest that feature selection, combined with ensemble learning models, can significantly enhance phishing website detection.  \nKeywords-Phishing website, ensemble tree, machine learning, feature selection, classification  \n1. INTRODUCTION  \nIn many nations, uninhibited entry to information outlets and public networks is considered a fundamental entitlement. The rise of the digital age has instigated substantial shifts in human behaviors, requiring adjustment. With the proliferation of ecommerce and online consumerism, phishing represents a significant cyber threat. The protection of our assets is anticipated to evolve into an essential element of contemporary civilization [1] . When users enter confidential information into imitation websites mirroring authentic ones, they inadvertently provide fraudulent entities access to their sensitive data, including credit card details, passwords, and other private information [2] .  \nPhishing remains a prevalent and perilous form of cyber-attack, posing substantial dangers in the contemporary digital landscape. With the increasing dependence on online platforms for various endeavors such as business operations, transactions, and healthcare services, susceptibility to phishing  \nattacks has risen [3] . These attacks involve the deceptive acquisition of personal and sensitive data, utilizing a combination of technical manipulation and social engineering strategies.  \nPhishing refers to a deceitful technique employed by attackers to acquire confidential information from unsuspecting individuals. Typically, phishing attacks leverage fraudulent emails or text messages, seemingly from trusted sources, deceiving unsuspecting individuals into divulging their confidential data [4] . These increasingly prevalent phishing websites mimic legitimate sites in appearance but are engineered to illicitly gather sensitive data provided by victims.  \nThe alarming surge in phishing incidents over recent years underscores the urgent need for enhanced cybersecurity measures. Statistics from the Anti-Phishing Working Group (APWG) reveal a staggering rise in phishing a","cbCainEDDJiFueKM","https://ap.wps.com/l/cbCainEDDJiFueKM","pdf",1100477,1,11,"English","en",105,"# Introduction\n## Phishing as a cybersecurity threat\n## Machine learning approaches for phishing detection\n## URL structure and feature relevance\n# Abstract\n## Research objectives and methods\n## Dataset and evaluation metrics\n## Results and implications","[{\"question\":\"What problem does the research address?\",\"answer\":\"The research addresses the challenge of detecting phishing websites accurately and efficiently as phishing tactics evolve and become more sophisticated.\"},{\"question\":\"Which models and methods are evaluated for phishing detection?\",\"answer\":\"The study evaluates feature selection techniques combined with tree-based ensemble models, specifically Random Forest and Extra Trees.\"},{\"question\":\"How is model performance measured in this research?\",\"answer\":\"Model performance is evaluated using metrics such as precision, recall, and accuracy on a dataset of phishing and legitimate websites.\"}]","Tree-based Ensemble Machine Learning for Phishing Website Detection - 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