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OverviewThe book involves the development of a web-based application that integrates multiple machine learning models-including XGBoost, Logistic Regression, and Gaussian Naive Bayes-to classify URLs as either phishing or legitimate. The models were trained using real world datasets consisting of over 5,000 phishing URLs and 5,000 legitimate ones, collected from trusted sources like Phish Tank and the University of New Brunswick. Key steps in the system include data preprocessing, feature selection, and feature extraction, focusing on elements like URL structure, domain age, and embedded scripts. The system leverages exploratory data analysis to visualize data insights and employs Principal Component Analysis (PCA) to optimize the model by reducing redundant data. Full Product DetailsAuthor: Dhairyashil More , Abhishek Ingalkar , Pratap KharabePublisher: LAP Lambert Academic Publishing Imprint: LAP Lambert Academic Publishing Dimensions: Width: 15.20cm , Height: 0.40cm , Length: 22.90cm Weight: 0.095kg ISBN: 9786208444563ISBN 10: 620844456 Pages: 60 Publication Date: 27 October 2025 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: Available To Order We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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