An entropy-guided hybrid framework for real-time phishing detection in digital communication systems

This study presents CyberSafe-EG-GWO, a hybrid framework for detecting phishing-based social engineering threats in digital communication platforms. The system integrates Entropy-Based Normalization Filtering (EBNF) to retain high-information tokens, Grey Wolf Optimization (EG-GWO) for discriminative feature selection, and SpinalNet for efficient classification. By combining statistical, contextual, and semantic embeddings, the framework achieves high accuracy while reducing feature dimensionality by 40–50%, supporting scalability for real-time deployment. Evaluations on the UCI Phishing Website and CIC-Phishing Email datasets demonstrate robust performance, with hold-out test accuracies of 95.8% and 94.3%, respectively, and average inference latency of 0.214 s per sample on GPU. Cross-validation highlights feature stability (> 85% overlap) and upper-bound accuracy of over 98%, while cross-dataset tests confirm generalization across domains. The design accommodates resource-constrained environments, with potential improvements via adaptive entropy thresholding, lightweight transformer embeddings, and interpretable decision explanations. CyberSafe-EG-GWO offers a reproducible, modular approach to real-time phishing detection, balancing precision, efficiency, and operational robustness, making it suitable for deployment in both structured and unstructured communication environments.