Enhanced phishing detection framework with tokenization WOA optimization domain features and CNN classification

Abstract

This study investigates the efficiency of numerous phishing detection techniques, concentrating on the proposed Convolutional Neural Network-based model that demonstrates superior performance over traditional and advanced methods. Attaining an exceptional 99.3% of accuracy, 99.93% of precision, 98.70% of recall, and 99.31% of F1 score, the typical leverages advanced methodologies such as the Whale Optimization Algorithm for feature selection and Zero Trust Verification to enhance robustness and efficiency. By detecting subtle patterns and differences, the CNN-based model outperforms traditional approaches like Decision Trees and KNN, as well as advanced models such as LSTM, making it a really reliable resolution for phishing detection. The model is further strengthened by its ability to integrate comprehensive feature analysis, adaptive learning, and dynamic classification capabilities. Proposed future enhancements include expanding datasets to encompass multilingual and diverse samples, incorporating hybrid models like transformers for improved feature extraction and optimizing the system for actual detection and deployment. These developments aim to recover the model’s scalability and applicability evolving cybersecurity environments, offering a comprehensive and reliable solution to effectively mitigate phishing threats in real-world scenarios.