An integrated comparative evaluation that combines six-algorithm benchmarking, leakage-free hyperparameter optimization, and SHAP-based interpretation on a public phishing dataset, offering practical guidance for security analysts is offered.
A Phishing Website Detection System Using a Stacked Hybrid Model With Explainable AI, designed to accurately classify websites as phishing or legitimate while providing transparent decision explanations, is presented.
A. Mounika, T. Siva Ramakrishna· International Journal for Re...· 0 citations
The system aims to classify URLs as either legitimate or malicious by analyzing various structural and statistical features extracted from the URLs by utilizing a Gradient Boosting Classifier due to its high accuracy and ability to handle complex patterns in data.
V. B, K. Rao, G. Prasad· International Scientific Jou...· 0 citations
An automated phishing detection system using Artificial Neural Networks to classify URLs as phishing or legitimate, outperforming Logistic Regression, SVM, SVM, and Random Forest baselines.
O. Omotosho, A. Adedoyin, O. Agboola et al.· 0 citations
Efficiency makes the proposed approach exceptionally suitable for real-time detection in resource-constrained environments, such as mobile applications or browser extensions, providing an accessible and proactive layer of defense for end-users.
The model proposed employs feature extraction using URLs, such as lexical and structural features like URL length, frequency of special characters, use of IP addresses, and occurrence of suspicious keywords, to improve the accuracy and reliability of detection.
Muna Rashid Hameed· Iraqi Journal for Computers...· 0 citations
External validation against ten official Iraqi university websites resulted in correct classification with no false positives, demonstrating that the proposed phishing detection model is reliable.
T. Omar· Engineering, Technology &...· 0 citations
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