Abstract:
Phishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and
organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and
fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rulebased
filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen
phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years,
Machine Learning (ML) and Artificial Intelligence (AI) have played a significant role in enhancing phishing detection. These
technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and
website behaviour. This research proposes a hybrid algorithm to detect phishing attacks based on ScC filter feature selection,
clustering, and classification ML methods: Deep Learning (DL) and Decision Tree (DT). Simulation results show that the proposed
technique achieves high percentage of accuracy in detecting phishing.