ENHANCING PHISHING DETECTION: A HYBRID DEEP LEARNING MODEL INTEGRATING BI-LSTM AND BI-GRU ALGORITHMS FOR URL AND CONTENT ANALYSIS

Abstract

In response to the pressing cybersecurity challenges posed by the proliferation of phishing URLs and malicious content, this research introduces a groundbreaking approach centered on transfer learning within deep neural networks. By leveraging transfer learning, intricate patterns within URLs and their content are unveiled, culminating in the development of a model seamlessly integrating Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) networks. These architectures effectively capture sequential dependencies, enhanced by their bidirectional variants accessing both past and future states to comprehend temporal dynamics and improve performance. Through meticulous evaluation and fine-tuning processes, the proposed cybersecurity solution demonstrates robustness and efficacy in defending against evolving threats. This research contributes significantly to advancing the cybersecurity domain, introducing an adaptive strategy that harnesses the strengths of Bi-LSTM and Bi-GRU networks within the framework of transfer learning, paving the way for more resilient and effective cybersecurity solutions.