Abstract
The goal of this study Phishing attacks are constantly evolving, and to avoid being detected by conventional means, attackers use cutting-edge approaches. Novelty detection aims to identify previously unseen phishing attacks, including zero-day threats and sophisticated evasion tactics. Phishing attacks continue to pose significant threats to cybersecurity, exploiting human vulnerabilities and developing quickly to avoid being detected by conventional methods. In response to these challenges, this literature survey presents a comprehensive review of phishing website detection techniques, focusing on novel approaches and the latest advancements in the field. It explores dynamic analysis, real-time monitoring, and anomaly detection techniques to keep pace with the ever-changing phishing landscape. The survey addresses the persistent issue of imbalanced datasets by presenting effective strategies for handling data from significantly more legitimate websites than phishing sites. It advocates for data augmentation, cost-sensitive learning, and domain adaptation to improve the accuracy and generalization of detection models. By highlighting the latest advancements and addressing key challenges, the review contributes to building robust and resilient phishing detection frameworks that safeguard users and organizations in the constantly evolving cyber threat landscape.