A State-of-the-Art Review on Phishing Website Detection Techniques

Phishing attacks remain a significant cybersecurity threat, with phishing websites serving as a primary tool for attackers to deceive users and steal sensitive information. The rapid evolution of phishing tactics has spurred the development of increasingly sophisticated detection mechanisms. This paper provides a comprehensive review of state-of-the-art techniques for phishing website detection, highlighting recent advancements in the field. In particular, it addresses emerging methods for detection, such as graph-based, large language model (LLM)-based approaches and phishing kit-based detection methods, which have not been extensively covered in previous surveys. By critically reviewing recent works from reliable databases, this study constructs a new taxonomy for phishing detection techniques. This review offers a comparison of these techniques, highlighting their strengths and limitations, and explores the challenges of real-world applications of these detection systems. Furthermore, the role of artificial intelligence (AI) in phishing website detection is discussed, and future research directions to improve detection capabilities are suggested. This work addresses emerging and uncovered phishing website detection methods in previous review papers and provides valuable insights for both researchers and practitioners working to develop more robust phishing website detection systems.