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In conclusion, this research proposed a hybrid phishing detection model that integrates Genetic Algorithm-based feature selection with multiple boosting classifiers, including XGBoost, AdaBoost, and Real AdaBoost, combined through a majority voting mechanism. The experimental results on two public phishing datasets demonstrated that the proposed model achieves very high detection accuracy and reliable performance.
Compared to existing models such as BMLSELM and MLSELM, the proposed model achieved better accuracy while using only 27 optimized features, compared to 33 and 48 features used in the benchmark models. The ensemble design also enabled the model to effectively handle common real-world challenges such as imbalanced datasets and redundant features.
Overall, the consistent strong performance across multiple datasets indicates that the proposed model is robust, efficient, and suitable for practical deployment in real-world phishing detection and cybersecurity applications. |
IEEE/ICACT20260061 Slide.19
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