Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1135-1140· 0 citations· 10 references
Abstract
The rapid growth of digital services in banking, e-commerce, education, and government sectors has significantly increased phishing attacks. Traditional blacklist-based detection systems are ineffective against zero-day phishing domains that imitate legitimate websites. To address this challenge, this paper proposes PHISHGUARD AI, a real-time phishing URL detection framework integrating a hyperparameter-optimized XGBoost classifier with explainable artificial intelligence (XAI) and community-driven threat intelligence. The framework utilizes lexical and structural features from URLs to calculate phishing probability using a tuned XGBoost classifier. Evaluation results on the PhiUSIIL dataset $(\mathbf{N}=\mathbf{2 3 5, 7 9 5})$ achieved 94.0% accuracy, 94.02% precision, 94.00% recall, and 94.01% F1-score. The explainability module improves transparency by identifying influential features responsible for each prediction. Finally, a community-based threat intelligence program enables users to validate and incorporate previously user-reported URLs into their machine learning models through continuous retraining cycles. Evaluation of the experimental data from this framework demonstrated a high detection accuracy with improved generalization for newly emerging phishing attacks. The proposed framework provides scalable and proactive phishing detection with improved interpretability.
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.
An Explainable Ensemble Artificial Intelligence Framework for Real Time Phishing Website Detection that addresses the primary weakness of existing systems and is deployed as a real-time desktop application.
F. J. Boniface· International Journal of Com...· 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.
Topicality. Phishing attacks remain one of the most common initial access vectors because they combine URL obfuscation, social engineering and rapid changes in domain infrastructure. From the perspective of computer science, the problem can be formulated as the classification of weakly structured textual objects; from the perspective of cybersecurity, it is a risk-reduction task aimed at preventing credential compromise, financial data leakage and malware delivery. The subject of the study is the automated detection of phishing URLs using lexical and structural features without opening or rendering the target webpage. The purpose of the article is to design and experimentally evaluate a lightweight URL pre-filtering model that can be integrated into an email gateway, browser extension or SOC monitoring module. The following results were obtained. The paper reviews current approaches to phishing mitigation and critically analyzes regulatory, organizational and machine-learning solutions. It is shown that many existing works do not sufficiently balance detection speed, interpretability and resistance to rapidly changing domains. The practical part includes the implementation of a feature extraction module and a simulation dataset of 1600 URLs. Logistic Regression, Decision Tree, Random Forest and Gradient Boosting were tested. The best F1 result was obtained by Random Forest: accuracy - 0.900, precision - 0.910, recall - 0.888, F1 - 0.899. Conclusion. Lexical URL features make it possible to build a fast and interpretable phishing pre-detection mechanism; however, such a model should be used as part of a layered cyber defense system rather than as a single decision-making control.
D. Abrosimov, D. Balagura, A.M. Yevheniev et al.· Terra security· 0 citations
Phishing attacks continue to pose a significant threat to individuals and organizations, driven by the increasing sophistication of cybercriminal techniques and the rapid expansion of digital services. Traditional detection approaches, such as blacklist-based and rule-based systems, are often ineffective against newly generated or obfuscated phishing URLs. This study proposes a machine learning (ML)-based framework intended for integration within penetration testing environments. The approach leverages multiple supervised learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and XGBoost, trained and evaluated using the PhiUSIIL Phishing URL Dataset, a large-scale benchmark dataset containing phishing and legitimate URL samples. A comprehensive preprocessing pipeline and feature engineering strategy are employed to enhance model performance. Experimental results demonstrate exceptionally high detection accuracy, with RF and XGBoost achieving near-perfect classification performance across key evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC. The proposed system is further designed for real-time deployment, enabling integration into penetration testing workflows for proactive security assessment. Despite promising results, limitations related to dataset characteristics and real-world generalization are acknowledged. Overall, this research highlights the effectiveness and practical applicability of ML-based approaches in strengthening phishing detection and advancing modern cybersecurity defences.
Ashwag Alotaibi, Mounir Frikha· International Journal of Adv...· 0 citations
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, D. Ramakrishna· International Journal for Re...· 0 citations