Advanced Machine Learning Approaches for Detecting Phishing Websites

Abstract-The development of new methodologies for identifying phishing attacks in the context of an increasing digital world is compromised due to lacking research and execution. This paper focuses on versatile approaches in Artificial Intelligence (AI) and Machine Learning (ML) to almost single-handedly eliminate phishing attempts. The work takes a holistic approach to problems of URL structure, content, and behaviors by XGBoost, LightGBM, Naïve Bayes, and CatBoost, as well as Graph Neural Network GNN. Multiple features are captured; for example, URL length, number of dots, slashes, numeric characters, and special characters will all be used for model training. Monitoring the system in real time and adapting it to new phishing paradigms makes it possible to tactically protect users and organizations from the continuous, unpredictable changes of cyber threats. This study covers the approach of employing diverse machine learning methods to combat phishing in a more direct and secure manner.