Securing The Digital Realm: Leveraging Transfer Learning In Ai-Driven Threat Detection For Enhanced Cybersecurity

Abstract

This paper presents a unique strategy based on transfer learning inside deep neural networks to address the urgent cybersecurity issues brought on by the growth of harmful links and phishing URLs. To construct a model that seamlessly mixes networks of bidirectional Gated Recurrent Units (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM), transfer learning is utilized to detect complicated links between URLs and their content.

With the help of its bidirectional variations, which access both past and future states, these architectures are able to better understand temporal dynamics and boost performance by means of rigorous assessment and optimisation procedures, the suggested cybersecurity solution exhibits resilience and effectiveness in countering constantly changing threats. By developing an adaptive method that utilises the advantages of both BiLSTM and BiGRU networks within the context of transfer learning, this research makes a substantial contribution to the advancement of the cybersecurity field and opens the door to the development of more robust and efficient cybersecurity solutions.