Improved Phishing Email Detection through Hybrid Machine Learning and AI-Based Data Augmentation

Abstract

Phishing attacks continue to evolve in sophistication, making them one of the most significant cybersecurity threats affecting individuals and organizations. Traditional phishing detection techniques based on rule-based systems and standalone machine learning algorithms often struggle to identify modern phishing emails generated using artificial intelligence and advanced social engineering techniques. This study proposes an AI-driven hybrid ensemble model that integrates Support Vector Machine (SVM), Random Forest (RF), and Bidirectional Long Short-Term Memory (Bi-LSTM) to improve phishing email detection accuracy and robustness. To address dataset imbalance and the limited availability of AI-generated phishing emails, an AI-based data augmentation module was incorporated to generate realistic synthetic phishing samples during model training. The proposed methodology consists of data collection, preprocessing, feature engineering, synthetic data generation, individual model training, and ensemble classification using a soft voting mechanism. Experimental evaluation was performed using benchmark phishing email datasets and standard performance metrics, including accuracy, precision, recall, F1-score, confusion matrix, and inference latency. Results indicate that the proposed hybrid model achieved an overall detection accuracy of 98.9%, outperforming a comparable existing model, which achieved 95.2% accuracy. Furthermore, the hybrid approach demonstrated improved generalization, reduced false classifications, and faster detection latency. The findings suggest that combining classical machine learning, deep learning, and AI-driven data augmentation provides an effective framework for protecting modern email systems against both conventional and AI-generated phishing attacks.