Abstract
Abstract: With the proliferation of mobile devices in recent years, there is a tendency to move almost all real-world activities to the cyber world. Although this makes our daily life easier, it violates many security rules due to the anonymous nature of the Internet. Phishing attacks are the easiest way to get sensitive information from innocent users. Phishers aim to obtain sensitive information such as usernames, passwords, and bank account information. Cybersecurity professionals are looking for reliable and consistent detection methods to detect phishing websites. This project deals with machine learning technology to detect phishing URLs by extracting and analyzing various features of legitimate and phishing URLs. Decision trees, random forests, and support vector machine algorithms are used to identify phishing websites in a two-step process of first visualizing and extracting features of URLs using python libraries and then training them into a model using Gradient Classifier Algorithm to predict real-time phishing websites
Published Version
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