A Survey on Phishing Attack Taxonomy, Detection Techniques, Datasets, and Security Measures
Kamaljeet Kaur, Ankit Kumar Jain · Journal of Applied Security Research · 2025
The rising phishing attack frequency in recent years has caused significant cybersecurity issues and presented a significant challenge for organizations and security professionals worldwide. Although several phishing detection methods have been proposed, they usually miss and cannot stop these changing attacks. Existing research contains constraints such as zero-day attacks, shortened URLs, and hidden harmful content. Often, these constraints lead to notable false positive and false negative rates, hence undermining the dependability of present techniques. This study offers a thorough examination of phishing detection research done over the last 10 years, from 2015 to 2024. The study examines phishing attack types, detection techniques, including heuristic, list-based, machine learning, and deep learning. Moreover, it emphasizes the dataset employed, the benefits, and the drawbacks of each. Building on the proposed taxonomy, this comprehensive article looks at present phishing detection running in phishing environments across mobile, website, and email platforms. The major objective is to identify study gaps and recommend paths for future work that can produce stronger, adaptive, and accurate phishing detection systems able to oppose developing threats.