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Now, I would like to detail the primary innovations that set our research apart. Our approach is built on three major technical contributions.
First, we implemented a multi-phase voting feature selection strategy. Rather than relying on a single method, we combined mutual information, recursive feature elimination, and model-based importance with correlation filtering. This ensures that only the most non-redundant and discriminative features move forward.
Second and perhaps most importantly, we propose a novel hybrid metaheuristic algorithm called hEVOSWO. By integrating Evolutionary Optimization with Spider Wasp Optimization, we are able to concurrently optimize both the feature subset and the SVM hyperparameters. This allows for a much more efficient exploration of the search space compared to standard algorithms.
Finally, we utilize a BiLSTM-based deep learning architecture. This is crucial because it allows the model to extract both sequential and contextual features from the URL string itself. When these deep embeddings are fed into our optimized SVM, the result is the high-performance detection framework you see here, designed to stay ahead of increasingly complex phishing attempts. |
IEEE/ICACT20260103 Slide.07
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