[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118971-en":3,"doc-seo-118971-105":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},118971,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Phishing Website Detection Using Machine Learning - A Review","Phishing website detection mitigates cyber-attacks where fraudulent websites or emails trick users into revealing passwords and financial data. A review synthesizes machine-learning approaches that analyze URL structure, website content, and keyword or pattern indicators to estimate phishing likelihood. Methods discussed include decision trees, support vector machines, and Random Forest, along with related feature sets such as URL layout and auxiliary signals like SSL certificate presence and domain age. The overall finding highlights machine learning as a practical safeguard against phishing threats.","Phishing Website Detection Using Machine Learning: A  \nReview  \nMarwa Abd Al Hussein Qasim (􀀍 )  \nCollege of Computer Science & Information Technology, Basrah University, Iraq  \n[itpg.marwa.qasim@uobasrah.edu.iq](itpg.marwa.qasim@uobasrah.edu.iq)  \nDr. Nahla Abbas Flayh  \nCollege of Computer Science & Information Technology, Basrah University, Iraq  \n[nahla.flayh@uobasrah.edu.iq](nahla.flayh@uobasrah.edu.iq)  \nAbstract—Phishing, a form of cyber-attack in which perpetrators employ fraudulent websites or emails to Deceive individuals into divulging sensitive information such as passwords or financial data, can be mitigated through various machine-learning algorithms for website detection.  \nThese algorithms, including decision trees, support vector machines, and Random Forest, analyze multiple website features, such as URL structure, website content, and the presence of specific keywords or patterns, to ascertain the likelihood of a website being a phishing site.  \nThis comprehensive review elucidates the concept of phishing website detection and the diverse techniques employed while summarizing previous studies, their outcomes, and their contributions. Overall, machine learning algorithms serve as a potent tool in the identification of phishing websites, thereby safeguarding users against falling prey to such malicious attacks.  \nKeywords—Phishing Detection, Machine learning, Phish Tank  \nI. Introduction  \nIn contemporary times, a substantial portion of the population is well aware of the utilization of the Internet for a multitude of purposes, including online banking, shopping, bill payments, and mobile device recharges. However, users engaging in these online activities often face a plethora of security concerns, ranging from cybercrime and spam to fraud and cyber terrorism, with phishing being just one among the various types of cybercrimes that are commonly perpetrated [1] .  \nThe objective of machine learning, which is a subfield of artificial intelligence, is to create systems that can improve and learn without explicit programming through experience [2] .  \nIn the field of machine learning, there are two distinct types of learning methodologies, namely supervised and unsupervised learning [3]. In supervised learning, the training dataset is composed of previous instances where both the input and output values are known and provided as labeled data. [4] .  \nOne approach to detecting phishing websites utilizing machine learning involves the utilization of supervised learning, where the training dataset exclusively comprises labeled data [5] .  \nThe process involves training a model with a dataset that encompasses both phishing and legitimate websites, enabling the model to acquire characteristics for distinguishing between the two types. Subsequently, the trained model can be employed to classify new websites as either phishing or legitimate, based on the learned features obtained from the training dataset. Notable features that can be leveraged for detecting phishing websites include the presence of specific words or phrases in the website's content or URL, the structure of the website's URL, and the overall layout and design of the website. Additionally, other features such as the presence of SSL certificates or the age of the domain may also prove valuable in the detection of phishing websites [6] .  \nThere exist multiple phishing detection techniques that utilize approaches such as white-listing, black-listing, content-based analysis, URL-based analysis, visual-similarity analysis, and machine-learning algorithms[7] .  \nTo effectively train a machine learning model to detect phishing websites, it is imperative to utilize a substantial and diverse dataset that encompasses both phishing and legitimate websites. Additionally, the trained model should be thoroughly evaluated and tested on a separate dataset to ascertain its accuracy and reliability in accurately discerning between phishing and legitimate websites [3] .  \n","cbCairK0tlG3BGoT","https://ap.wps.com/l/cbCairK0tlG3BGoT","pdf",408650,1,12,"English","en",105,"# Introduction\n## Machine learning background (supervised vs. unsupervised)\n## Phishing detection techniques and key features\n# Phishing Website Detection\n## URL analysis\n## Content analysis\n## Data requirements and evaluation","[{\"question\":\"What is phishing website detection using machine learning intended to accomplish?\",\"answer\":\"It aims to classify websites as phishing or legitimate by learning distinguishing patterns that indicate fraudulent intent, reducing the risk of users exposing sensitive information.\"},{\"question\":\"Which machine-learning methods are highlighted for phishing website detection?\",\"answer\":\"The review highlights decision trees, support vector machines, and Random Forest as algorithms that can learn from website features to detect phishing.\"},{\"question\":\"What kinds of features can models use to detect phishing websites?\",\"answer\":\"Models can rely on URL structure, website content including suspicious words or phrases, and other signals such as SSL certificate presence and domain age.\"}]","Phishing Website Detection Using Machine Learning - 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