Detection of Phishing Activities Using Deep Learning Approaches

The Phishing attacks remain a persistent and evolving threat to cyber-security, targeting human vulnerabilities and causing billions of dollars in financial losses annually. This paper provides a comprehensive analysis of the accuracy, efficiency, and scalability of deep learning-based approaches for phishing detection. We evaluate the performance of models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) using real-world datasets, assessing key metrics like precision, recall, and detection speed. Our findings reveal that deep learning techniques outperform traditional rule-based and machine learning methods, achieving higher detection rates with fewer false positives. Additionally, we discuss the practical implications of integrating deep learning models into real-time phishing detection systems and highlight challenges such as computational complexity and data quality. This study demonstrates the transformative potential of deep learning in strengthening cyber defence mechanisms against increasingly sophisticated phishing attacks.