PHISHING WEBSITE DETECTION SYSTEM USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract: Phishing websites are one of the most common cybersecurity threats used by attackers to steal sensitive information such as usernames, passwords, banking details, and personal data. These fake websites are designed to look similar to legitimate websites, making it difficult for users to identify them manually. Traditional phishing detection methods such as blacklist-based and rule-based systems are not effective for newly generated or unknown phishing websites.

This research presents a Phishing Website Detection System using Artificial Intelligence and Machine Learning. The proposed system analyzes different URL and website-based features such as URL length, number of special characters, suspicious keywords, domain-related attributes, login form presence, iframe usage, redirection behavior, and external links. Machine learning models such as Random Forest, XGBoost, and Multi-Layer Perceptron are used for classification. A hybrid ensemble voting model is applied to improve prediction accuracy and reliability.

The system is implemented as a Flask-based web application where users can enter a website URL and receive an instant prediction result. The output includes the classification result, phishing probability, risk level, important feature values, scan history, and downloadable PDF report. Experimental results show that the ensemble model performs better than individual classifiers and provides an effective solution for phishing website detection.

Keywords: Phishing Detection, Artificial Intelligence, Machine Learning, Cybersecurity, URL Analysis, Flask, Ensemble Learning, Website Security.