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To wrap up our presentation, we have introduced a hybrid framework for phishing URL detection that effectively bridges the gap between deep temporal learning and advanced metaheuristic optimization. By integrating a four-stage feature selection process with a BiLSTM network for deep embedding extraction, and optimizing the final classifier through our custom hEVOSWO algorithm, we have achieved a system that offers both high classification accuracy and remarkable computational efficiency. Our experimental results on the ISCX-URL2016 benchmark speak for themselves: reaching a peak accuracy of 97.31% and an ROC AUC of 0.9867. These results confirm that combining deep sequential learning with precise optimization makes this framework highly suitable for real-world cybersecurity deployment. Looking ahead, our Future Work will focus on bringing this research into the hands of users through real-time deployment as a browser extension or API. We also plan to expand the models capabilities to support multilingual and obfuscated URLs, integrate transformer-based encoders, and conduct rigorous evaluations under adversarial phishing scenarios to ensure long-term robustness. |
IEEE/ICACT20260103 Slide.22
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