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To truly validate our research, we conducted a comprehensive benchmark analysis against the most recent and prominent state-of-the-art models in the field. As you can see in this comparison table, our BiLSTM with hEVOSWO framework outperforms existing methodologies across every key metric. While advanced models like the RoBERTa and LSTM hybrid by Jishnu et al. achieved a strong accuracy of 97.14%, our approach reaches a superior 97.31%. We also see significant leads over traditional machine learning models, such as the Random Forest model by Putri et al., which stands at 93.7%, and the KNN model by Irsan et al. at 95%. What is most notable is our Precision of 0.9742 and F1-Score of 0.9730. These are the highest in the cohort, proving that our hybrid optimization doesnt just improve raw accuracy, it creates a more reliable, balanced, and high-performance system than currently available alternatives. This comparative success highlights the effectiveness of our novel hEVOSWO algorithm in fine-tuning deep learning architectures for critical cybersecurity tasks. |
IEEE/ICACT20260103 Slide.20
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