 |
Once our hEVOSWO algorithm has completed its 100 iterations, we move to the final stage: Classifier Integration. The primary step here is Final Training. We take the specific Optimal Features Parameter and the best feature mask discovered during the optimization phase and use them to retrain our final SVM model using the RBF kernel. This ensures that the classifier is not just using generic settings, but is perfectly tuned to the deep temporal patterns identified by the BiLSTM. Finally, we perform a rigorous Evaluation to ensure real-world readiness. We validate the model¡¯s generalizability on a 25% stratified holdout set. This separate, unseen data is what produces our final classification of a URL as either PHISHING or LEGITIMATE, confirming that our hybrid framework can handle new threats with the high accuracy we are about to see in our results.
|
IEEE/ICACT20260103 Slide.16
[Big Slide]
[YouTube]