Enhancing Cybersecurity Through AI-Driven Threat Detection: A Transfer Learning Approach

Abstract

In today's digital landscape, fortifying cyber security is of utmost importance. This research introduces an innovative strategy that relies on transfer learning within deep neural networks to combat evolving threats, with a specific focus on phishing URLs and Malicious links, a major vector for cyber-attacks. We meticulously curate a diverse dataset of phishing and legitimate URLs, subjecting it to rigorous pre-processing. Departing from traditional methods, we leverage transfer learning to extract intricate patterns within URLs and their content. Our unique approach integrates transfer learning into a hybrid model, combining deep learning techniques with the power of transfer learning. This hybrid model employs soft and hard voting to optimize phishing threat detection accuracy and efficiency. We fine-tune our models with advanced feature selection and hyper parameter optimization, using rigorous evaluation metrics to assess performance