Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each delineated by 30 attributes. The WOA’s hyperparameter optimization enhances the RNN’s performance, evidenced by a meticulous validation process. The results, encapsulated in precision, recall, and F1-score metrics, surpass baseline models, achieving an overall accuracy of 92%. This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.