# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 4 Simulation and Results
### Model Performance Metrics  
The performance of EffiGRU-GhostNet was compared with baseline models, including GRU-RNN, LSTM, ANN-GWO, CNN, SVM, and DBN. Different performance metrics were evaluated for both datasets. On the IoT-23 and APA-DDoS datasets, Tables 4 and 5 provide the performance metrics of several machine-learning models. These tables use various metrics to compare EffiGRU-GhostNet against GRU-RNN, LSTM, ANN-GWO, CNN, SVM, and DBN. Precision, Log Loss, recall, Kappa Statistic, F1-score, G-mean, specificity, MCC, average precision, and accuracy are reviewed. The IoT-23 dataset performance metrics are shown in Table 4. In practically all criteria, the EffiGRU-GhostNet model outperforms competitors. EffiGRU-GhostNet ranks top in precision (0.9792), accuracy (0.9788), and G-mean (0.9854) for spotting significant patterns in the IoT-23 dataset. In all models, EffiGRU-GhostNet has the lowest Log Loss, 0.2289. Prediction accuracy is better with less uncertainty. The APA-DDoS dataset performance metrics are shown in Table 5. The EffiGRU-GhostNet model performs well, like IoT-23. Precision (0.9905), accuracy (0.9999), and G-mean (0.9966) are the highest. Its high specificity (0.9885) and recall (0.9910) show that the model can identify positive and negative scenarios. The lower Log Loss (0.2435) indicates that EffiGRU-GhostNet produces better accurate probability estimates than the other models. Both tables show the EffiGRU-GhostNet model's excellent performance on both datasets. Deleting irrelevant components reduced complexity without compromising accuracy. EffiGRU-GhostNet's efficiency makes it a trustworthy IoT and DDoS detector. EffiGRU-GhostNet has proven useful by successfully identifying NTL DDoS attacks with a low false positive rate and high accuracy, although the datasets used emphasize NTL traits.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Proposed** & **GRU-RNN [16]** & **LSTM [27]** & **ANN-GWO [22]** & **CNN [18]** & **SVM [9]** & **DBN [19]** \\ \hline Precision & 0.9792 & 0.8703 & 0.8591 & 0.8802 & 0.8225 & 0.8322 & 0.9255 \\ \hline Log Loss & 0.2289 & 0.3342 & 0.3453 & 0.3228 & 0.4055 & 0.3928 & 0.3042 \\ \hline Recall & 0.9789 & 0.8686 & 0.8545 & 0.8735 & 0.8144 & 0.8241 & 0.9499 \\ \hline Kappa statistic & 0.9789 & 0.8641 & 0.8525 & 0.8735 & 0.8173 & 0.8268 & 0.9192 \\ \hline F1 Score & 0.9790 & 0.8685 & 0.8568 & 0.8765 & 0.8187 & 0.8287 & 0.9392 \\ \hline G-mean & 0.9854 & 0.9143 & 0.8986 & 0.9187 & 0.8662 & 0.8764 & 0.9351 \\ \hline Specificity & 0.9788 & 0.9186 & 0.9062 & 0.9254 & 0.8725 & 0.8841 & 0.9006 \\ \hline MCC & 0.9790 & 0.8642 & 0.8523 & 0.8741 & 0.8165 & 0.8262 & 0.8497 \\ \hline Average Precision & 0.9791 & 0.8745 & 0.8628 & 0.8832 & 0.8356 & 0.8451 & 0.9284 \\ \hline Accuracy & 0.9788 & 0.8643 & 0.8554 & 0.8691 & 0.8084 & 0.8201 & 0.9343 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance values on the IoT-23 dataset.  
Figure 8: Comparison of feature selection results from IoT-23 and APA-DDoS datasets. (**a**) Top 16 features selected by [PERSON] from IoT-23 dataset. (**b**) Top 10 features selected by [PERSON] from APA-DDoS dataset.