Facilitating Secure Web Browsing by Utilizing Supervised Filtration of Malicious URLs

Nowadays, Internet use by all people has become a daily matter, whether to do business, buy online, education, entertainment, or social communication. Therefore, attackers exploit users of the Internet by tricking them into clicking on a specific link to carry out a phishing attack on them to collect sensitive information for users, whether credentials, usernames, email passwords banks, electronic payment information via a phishing attack that targets the end user, so many methods have appeared for protection from this type of attack, and among these methods using algorithms of machine learning (ML) to distinguish between the proper URL from the malicious phishing using several types from algorithm classification based on certain features found in any URL and using a ready-made data set, the best types of algorithm best performance are determined. It is recommended to use them based on the accuracy of the algorithm. We applied different machine learning algorithms on the same data set as Decision Tree, Logistic Regression, Linear Discriminant, Gradient, and Random Forest. The best accuracy was for the RF algorithm, with 97%.