Safeguarding your Data from Malicious URLs using Machine Learning

Abstract

Abstract: Malicious URLs host a wide range of unwanted content and can be extremely dangerous to potential victims. Thus, a quick and effective detection method is required. The topic of identifying harmful URLs based on data gleaned from URLs using machine learning methods is the main subject of this thesis. The simplest method of obtaining sensitive information from unwitting people is through a phishing attack. The goal of phishers is to obtain crucial data, such as username, password, and bank account information. Cybersecurity professionals are now looking for stable and reliable detection techniques to detect phishing websites. In order to distinguish between legal and phishing URLs, this article uses machine learning technology. It extracts and analyses many aspects of both types of URLs. Algorithms such as Support Vector Machine, Decision Tree, and Random Forest are used to identify phishing websites. By evaluating each algorithm's accuracy rate, false positive and false negative rates, the study aims to identify phishing URLs and identify the best machine learning method.