Intelligent Phishing Url Detection: A Solution Based On Deep Learning Framework

Abstract

On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial status quickly. The attacker uses their credibility or sensitive information to harm the target or victim. The attacker is clever and uses different strategies to fetch user-sensitive information. The existing techniques fail to overcome these issues to some extent. This work focuses on discovering the essential features that help to differentiate the legitimate and illegitimate URLs. We applied a deep learning technique on the benchmark datasets to identify the pattern of phishing URLs. We used gradient boosted decision trees algorithm to train our model and applied the regular deeply connected neural network layers in various sequences and Adam optimizer. The most found patterns will help the system to detect phishing URLs and avoid phishing. We consider the accuracy, Ff-score, and Root Mean Square Error (RMSE) as our evaluation metrics for model evaluation. The results show that the trained model can achieve an approximately 92% accuracy and 94% f-score.