Phishing Detection Methods

Abstract

This chapter explores the evolution of phishing detection methods, present traditional, advanced, and hybrid approaches. Traditional methods provide a base layer of defense, but their effectiveness is limited against adaptive attacks. Advanced techniques employ machine learning (ML) and deep learning (DL) to enhance detection accuracy and leveraging data-driven models that analyze URL structures, email content, and website behavior. Hybrid models combine multiple techniques to optimize performance. Case studies illustrate the practical application of these methodologies in various domains which include the use of convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and ensemble algorithms such as Random Forests and Gradient Boosting, achieving detection accuracies exceeding 97% in many cases. Research also highlights on large language models (LLMs) and Universal Adversarial Perturbation (UAP) techniques, for detecting advanced phishing strategies. Challenges in phishing detection include imbalanced datasets and real-time detection requirements.