Enhancing Cyberspace Security with Phishing Detection and Defense Using Machine Learning Models

In response to the evolving aspect of emerging phishing threats, this paper presents a comprehensive study of machine learning techniques used for detecting phishing attacks. In this process, we investigate and explore the various stages of phishing attacks, highlighting the techniques and strategies used by attackers. In addition, existing detection methods are catego-rized and concisely reviewed. To ensure the applicability of our findings in real-life scenarios, simulations are performed using two comprehensive real-world datasets. Moreover, the detection mechanism is implemented using several advanced machine-learning models, including state-of-the-art Natural Language Processing (NLP). The study also bottom-lines the significance of feature engineering in improving model performance. Our contributions will help safeguarding cyberspace through the use of machine learning technologies.