Combatting Phishing Attacks: Leveraging Machine Learning for Real-Time Detection in Penetration Testing

Abstract

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.