A WEB APPLICATION FOR REAL-TIME PHISHING WEBSITE DETECTION

Abstract

The rapid proliferation of the internet has led to an alarming increase in cyber threats, particularly phishing attacks. Phishing websites impersonate legitimate ones, luring users into divulging sensitive information such as login credentials and financial details. Detecting and preventing such attacks are crucial for safeguarding user privacy and security. This paper presents the development of a web application for real-time phishing website detection. The application employs machine learning techniques, specifically the XGBoost algorithm, to analyze the characteristics of URLs and classify them as either legitimate or phishing. It offers users a simple and effective way to check the authenticity of websites before interacting with them.The web application provides an intuitive user interface, allowing users to enter a URL and receive an instant assessment of its legitimacy. The underlying machine learning model is trained on a diverse dataset of legitimate and phishing URLs, ensuring robust detection capabilities. Additionally, the application provides insights into the features used for classification, enhancing user awareness of potential threats. The proposed web application serves as a valuable tool in the fight against phishing attacks, empowering users to make informed decisions when navigating the web. Its real-time detection capabilities and user-friendly interface make it accessible to a wide audience, contributing to a safer online environment. KEYWORDS Web Application, Phishing Detection, Machine Learning, XGBoost, User Privacy.