[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119302-en":3,"doc-seo-119302-105":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},119302,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Phishing detection using clustering and machine learning","Phishing is a pervasive cyber threat that targets human psychology and technical weaknesses to obtain sensitive credentials and personal data. Existing signature- and rule-based defenses struggle as attackers continuously change tactics and launch previously unseen campaigns. This study proposes a hybrid phishing detection approach that combines clustering with classification using deep learning and decision trees. Experiments on phishing datasets evaluate accuracy and demonstrate strong detection performance through statistical comparison with established methods.","Phishing detection using clustering and machine learning  \nLuai Al-Shalabi, Yahia Hasan Jazyah  \nFaculty of Computer Studies, Arab Open University, Ardiya, Kuwait  \nArticle history:  \nReceived Dec 28, 2023 Revised May 16, 2024 Accepted Jun 1, 2024  \nKeywords:  \nArtificial intelligence Decision tree  \nDeep learning Machine learning Phishing  \nCorresponding Author:  \nPhishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rule-based filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years, machine learning (ML) and artificial intelligence (AI) have played a significant role in enhancing phishing detection. These technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and website behavior. This research proposes a hybrid algorithm to detect phishing attacks based on clustering and classification machine learning methods (CMLM): deep learning (DL) and decision tree (DT) . Simulation results show that the proposed technique achieves a high percentage of accuracy in detecting phishing.  \nThis is an open access article under the CC BY-SA license.  \nYahia Hasan Jazyah  \nFaculty of Computer Studies, Arab Open University St. Mohammed Nazzal Al-Moassab, Ardiya, Kuwait [Email: yahia@aou.edu.kw](Email: yahia@aou.edu.kw)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nPhishing is a pervasive and insidious form of cybercrime that preys on human psychology and technical vulnerabilities. It involves the use of deceptive techniques to trick individuals or organizations into divulging sensitive information, such as login credentials, financial details, or personal data. Phishing attacks are often the initial entry point for broader cyber threats, including identity theft, fraud, and malware infections. To combat this growing menace, effective phishing detection methods have become indispensable.  \nThe sophistication of phishing attacks continues to evolve, making it a challenging task to thwart these threats. Cybercriminals utilize a variety of tactics, including misleading emails, fraudulent websites, and social engineering strategies that exploit human trust and curiosity. The dynamic nature of these attacks means that traditional, static security measures are often ineffective. This has led to the development of advanced and adaptive techniques for detecting and mitigating phishing attempts.  \nPhishing detection involves the identification and prevention of deceptive or malicious content within emails, websites, or other digital communication channels. It encompasses a broad spectrum of methods, ranging from rule-based filters and signature-based systems to more advanced approaches that leverage artificial intelligence (AI), machine learning (ML), and behavioral analysis. As cybercriminals constantly refine their tactics to bypass conventional defenses, the need for innovative and responsive detection mechanisms has become increasingly pressing.  \nIn this context, this paper explores the landscape of phishing detection, addressing both the existing challenges and the latest advancements in the field. And proposing a hybrid algorithm that merges between clustering and classification using deep learning (DL) and decision tree (DT) for two different datasets (DSs) .  \nThe main contributions of this work are summarized in three-fold:  \n− A robust hybrid algorithm to detect phishing attacks using clustering, classification, and stabilitycorrelation and correlation (ScC) fea","cbCait0Y6LJuSQu6","https://ap.wps.com/l/cbCait0Y6LJuSQu6","pdf",632551,1,11,"English","en",105,"# Introduction\n## Phishing challenges and evolving attacks\n# Phishing detection algorithms’ comparisons\n## Accuracy and comparative evaluation\n# Preliminaries\n## Feature selection methods\n# Proposed hybrid algorithm\n## Clustering and classification with DL and DT\n# Complexity analysis\n# Conclusion","[{\"question\":\"Why are traditional phishing detection methods often ineffective?\",\"answer\":\"Traditional signature-based and rule-based filtering techniques are limited because attackers adapt their tactics and create new phishing campaigns that bypass static defenses.\"},{\"question\":\"What approach does this paper propose for phishing detection?\",\"answer\":\"The paper proposes a hybrid algorithm combining clustering with classification using deep learning and decision trees, supported by feature selection methods to improve speed and simplicity.\"},{\"question\":\"How is the proposed method evaluated?\",\"answer\":\"The work includes thorough statistical analysis on well-known phishing datasets and compares the results with other published detection methods, including combinations using DL/DT with feature selection strategies such as ScC and PCA-related approaches.\"}]","Phishing detection using clustering and machine learning | 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are traditional phishing detection methods often ineffective?","Question",{"text":75,"@type":76},"Traditional signature-based and rule-based filtering techniques are limited because attackers adapt their tactics and create new phishing campaigns that bypass static defenses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does this paper propose for phishing detection?",{"text":80,"@type":76},"The paper proposes a hybrid algorithm combining clustering with classification using deep learning and decision trees, supported by feature selection methods to improve speed and simplicity.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed method evaluated?",{"text":84,"@type":76},"The work includes thorough statistical analysis on well-known phishing datasets and compares the results with other published detection methods, including combinations using DL/DT with feature selection strategies such as ScC and PCA-related 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