Adaptive Transfer Learning for Robust Phishing Attack Detection Using Recurrent Layers: Enhancing Cybersecurity Through Dynamic Defense Mechanisms

Abstract

This paper proposes a robust phishing detection framework the use of adaptive switch getting to know mixed with recurrent layers, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs). Phishing assaults pose a significant chance to cybersecurity, and conventional detection techniques have struggled to maintain pace with the dynamic nature of those assaults. The proposed framework leverages the strength of switch getting to know to conform to new phishing styles with out requiring widespread retraining. By integrating recurrent layers, the model captures temporal dependencies inherent in phishing emails and verbal exchange styles, making an allowance for more accurate detection of evolving threats. The framework is designed to enhance cybersecurity by way of dynamically adjusting to new phishing approaches, supplying a scalable and effective solution for phishing detection.