Phishing Website Detection using Machine Learning

Abstract

Phishing is a major cybersecurity threat in which attackers create fake websites that closely resemble legitimate ones to steal sensitive user information such as login credentials, banking details, and personal data. With the increasing use of online services, phishing attacks have become more sophisticated and difficult to detect using traditional methods like blacklisting and rule-based systems. These conventional approaches often fail to identify newly created or zero-day phishing websites, making it necessary to develop more intelligent and adaptive detection techniques. This project proposes a machine learningbased approach for detecting phishing websites by analyzing various features such as URL characteristics, domain information, and security indicators. Different classification algorithms are trained on a dataset of phishing and legitimate websites to build an efficient prediction model. The system is capable of automatically classifying websites with high accuracy, helping users avoid potential threats. This approach not only enhances detection performance but also provides a scalable and effective solution for improving online security.