DEPHIDES: Deep Learning Based Phishing Detection System

Abstract

In today's digital landscape, the increasing prevalence of internet-connected devices, including smartphones, personal computers, and IoT devices, has enabled users to perform a wide range of daily activities such as shopping, banking, and communication in the online world. However, cybercriminals are capitalizing on the Internet's anonymity and the ease of conducting cyberattacks. Phishing attacks have become a popular method for acquiring sensitive user information, including passwords, bank account details, social security numbers and more, often through social engineering and messaging tools. To protect users from such threats, it is essential to establish sophisticated phishing detection systems on computing devices. Many of these systems leverage machine learning techniques for accurate classification. In recent years, deep learning algorithms have gained prominence, especially when dealing with large datasets. This study presents the development of a phishing detection system based on deep learning, employing five different algorithms: artificial neural networks, convolutional neural networks, recurrent neural networks, bidirectional recurrent neural networks, and attention networks. The system primarily focuses on the fast classification of web pages using URLs. To assess the system's performance, a relatively extensive dataset of labeled URLs, comprising approximately five million records, was collected and shared. The experimental results indicate that convolutional neural networks achieved the highest performance, boasting a detection accuracy of 98.74% for phishing attacks. This research underscores the effectiveness of deep learning algorithms, particularly in enhancing cybersecurity in the face of evolving cyber threats.