Enhancing Phishing Detection, Leveraging Deep Learning Techniques

Abstract

The widespread usage of internet-connected gadgets has led to a transformation in how individuals engage with technology, facilitating easy participation in various online activities such as social media, banking, and shopping. However, this proliferation has also provided an opportunity for fraudsters to exploit the Internet's anonymity through elaborate phishing schemes. These schemes aim to deceive users into disclosing personal information, including passwords and bank account details, often employing social engineering techniques. Consequently, the development of sophisticated phishing detection systems has become imperative to safeguard users' financial assets and digital identities. Many of these systems leverage state-of-the-art technology, with a significant reliance on machine learning methods for accurate and rapid detection of phishing attempts. Among these methods, deep learning algorithms have gained prominence due to their ability to efficiently process and analyze vast amounts of data. This paper presents a unique phishing detection system grounded in deep learning principles, employing a diverse array of techniques such as CNNs, attention networks, neural architects, and recurrent neural networks. The system's efficacy in real-time phishing attack detection is demonstrated through its focus on swift categorization of web pages based on URLs. Evaluation of the proposed method using a large dataset comprising nearly five million tagged URLs underscores its effectiveness.