PHISHING WEBSITE DETECTION USING MACHINE LEARNING

Abstract

Criminals seeking sensitive information construct illegal clones of actual websites and e mail accounts. The e-mail will be made up of real firm logos and slogans. When a user clicks on a link provided by these hackers, the hackers gain access to all of the user’s private information, including bank account information, personal login passwords, and images. Random Forest and Decision Tree algorithms are heavily employed in present systems, and their accuracy has to be enhanced. The existing models have low latency. Existing systems do not have a specific user interface. In the current system, different algorithms are not compared. Consumers are led to a faked website that appears to be from the authentic company when the e-mails or the links provided are opened. The models are used to detect phishing Websites based on URL significance features, as well as to find and implement the optimal machine learning model. Logistic Regression, Multinomial Naive Bayes, and XG Boost are the machine learning methods that are compared. The Logistic Regression algorithm outperforms the other two. The goal was to get as many people to click on a link or open an infected file as possible. There are various approaches to detect this type of attack. One of the approaches is machine learning. The URL’s received by the user will be given input to the machine learning model then the algorithm will process the input and display the output whether it is phishing or legitimate. There are various ML algorithms like SVM, Neural Networks, Random Forest, Decision Tree, XG boost etc. that can be used to classify these URLs. The proposed approach deals with the Random Forest, Decision Tree classifiers. The proposed approach effectively classified the Phishing and Legitimate URLs with an accuracy of 87.0% and 82.4% for Random