A Systematic Review on Website Phishing Attack Detection for Online Users
Kirti V. Deshpande, Jaibir Singh · International Journal of Image and Graphics · 2025
Phishing is the criminal effort to steal delicate information such as account details, passwords, usernames, credit, and debit card details for malicious use. Phishing fraud might be the most popular cybercrime used today. The online and webmail payment sectors are highly affected by phishing attacks. Nowadays, attackers create many techniques that pave the way for them to steal all personal information from the selected victims easily. However, numerous anti-phishing techniques are used to detect the phishing attack, such as blacklist, visual similarity, heuristic detection, Deep Learning (DL), and Machine Learning (ML) techniques. ML techniques are more efficient at detecting phishing attacks, and these techniques also rectify the drawbacks of existing approaches. This paper provides a detailed review of various phishing techniques, encompassing phishing mediums, phishing vectors, and numerous technical approaches. Also, new research works are analyzed to detect phishing websites using heuristic, visual similarity, DL, and ML models. Neural Networks (NNs), Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machine (SVM), fuzzy logic methods, Long Short-Term Memory (LSTM) techniques, Random Forest (RF), Decision Tree (DT), Adaboost –Extra Tree (AET) classifiers based on ML models are examined in this review paper.