PHISHING URL AND WEBSITE DETECTION USING ML

ABSTRACTPhishing attacks remain a significant threat in the digital landscape, with cybercriminals constantlydeveloping sophisticated techniques to deceive users into revealing sensitive information. This studypresents a robust framework for phishing URL and website detection utilizing the XGBoost (ExtremeGradient Boosting) algorithm, known for its superior performance and efficiency in classificationtasks. The proposed system focuses on analyzing various features extracted from URLs and webpagecontent, including lexical, structural, and host-based attributes, to distinguish between legitimate andmalicious sites. By leveraging the strengths of the XGBoost algorithm, the framework aims to improvedetection accuracy and reduce false positive rates, thereby enhancing user safety in onlineenvironments.The research involves a comprehensive evaluation of the XGBoost model's performanceagainst several benchmark datasets representative of real-world phishing scenarios. The model istrained on a diverse set of features, with hyperparameter tuning conducted to optimize its predictivecapabilities. Results indicate that the XGBoost-based approach achieves a high classification accuracywhile demonstrating robustness against imbalanced datasets common in phishing detection tasks. Thefindings underscore the effectiveness of machine learning techniques in cybersecurity applications andhighlight the potential for implementing the XGBoost algorithm in real-time phishing detectionsystems to safeguard users from online threats. Keywords: XGBoost (Extreme Gradient Boosting), Classification, Feature Selection, PhishingDetection, Training, Model Accuracy.