HawkPhish-DNN cybersecurity model: adaptive hybrid optimization and deep learning for enhanced multi-objective phishing URL detection

Abstract

Phishing attacks continue to pose significant security risks, necessitating effective detection methods to safeguard users. This paper presents HawkPhish-DNN cybersecurity model, a novel phishing detection framework that integrates Harris Hawk Optimization (HHO) with a Deep Neural Network (DNN). In the preprocessing phase, redundant URLs and domain features are removed, while URL length and entropy are extracted to form an efficient feature set. The detection model employs advanced neural layers, including Sigmoid and ReLU, to enhance learning and classification. By leveraging multi-objective HHO, HawkPhish-DNN optimizes accuracy and mitigates false positives through a time-varying penalty function, Pareto dominance, and crowding distance strategies. Empirical findings demonstrate that HawkPhish-DNN cybersecurity model achieves an accuracy of up to 99.6% and a false positive rate as low as 0.2% on benchmark datasets. Additionally, it maintains low computational overhead, ensuring practicality for real-time deployment. These results highlight the potential of HawkPhish-DNN cybersecurity model in providing a robust, user-friendly defense against phishing threats without inflating false alarms.