DL-Phish: Optimized Features Learning for Phishing URL Detection Using Deep Neural Networks

Abstract

Phishing persists as a serious cybersecurity concern in which consumers are tricked into divulging private information by using phony websites. It is necessary to accurately and competently identify such hazardous links in order to secure the internet environment. Because Deep Learning (DL) approaches can automatically learn complex patterns, and proved to be effective tools for identifying such attacks. Five DL models were tested in this study using a dataset gathered from Kaggle: Recurrent Neural Network (SimpleRNN), Long Short-Term Memory (LSTM), Multi-Layer Perception (MLP), Conventional Neural Network (CNN), and Gated Recurrent Unit (GRU). The most useful URL attributes were selected using Random Forest-based feature significance techniques and Chi-Square feature selection to maximize the model’s efficiency. To achieve the global performance study, the models were evaluated using a variety of assessment metrics, including accuracy, precision, recall, and F1 score, with the help of loss graphs and confusion matrices. LSTM successfully proved its effectiveness in dealing with sequential patterns in phishing URLs with a highly accurate result of 98.80%. The DL models given in the paper, especially the recurrent neural networks, performed significantly better compared to the highest standard of accuracy and reliability established in the previous papers. The experimental results confirm that a reliable method for identifying phishing URLs can be formed using recurrent DL and effective feature selection.