## 4 Experimental Results
### Analysis of Detection Results  
Our simulations were conducted using Python 3.9, leveraging libraries such as PyTorch 1.11 for model implementation and training and scikit-learn 1.5.2 for evaluation metrics. The experiments were run on a system equipped with an Intel i7 processor and \(16\) GB of RAM. As depicted in Tables 4-6, the proposed ROSE-BOX algorithm demonstrates intrusion detection capabilities across three cybersecurity benchmark datasets, CIC-IDS2017, CSE-CIC-IDS2018, and CIC-DDoS2019, achieving superior detection accuracy and operational efficiency compared to contemporary machine learning paradigms. In order to more intuitively compare the intrusion detection performance of ROSE-BOX with other methods on the three datasets, we used bar charts to visualize their accuracy, precision, recall, F1 score, and AUROC value, as shown in Figure 6, Figure 7, and Figure 8, respectively. Through comprehensive evaluation against nine baseline methodologies spanning classical ensemble learning (Random Forest [25], AdaBoost [10], and XGBoost [12]), deep neural architectures (DNN [26], CNN-LSTM [12], and Transformer [7]), and hybrid approaches (XGB-RF [35]), our framework exhibits enhanced robustness in identifying zero-day attacks while maintaining sub-second inference latency. Particularly noteworthy is its consistent performance advantage over temporal models, such as LSTM [11], and proximity-based classifiers (KNN [24]), establishing ROSE-BOX as a versatile solution for real-time network intrusion detection across diverse threat landscapes. Figure 8: Comparison of ROSE-BOX with other models using CIC-DDoS2019. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & _Accuracy_ & _Precision_ & _Recall_ & _F1 Score_ & _AUROC_ \\ \hline KNN [24] & 90.82\% & 47.829\% & 45.243\% & 46.095\% & 0.73662 \\ RF [25] & 92.636\% & 48.948\% & 48.549\% & 48.682\% & 0.74064 \\ AdaBoost [10] & 65.706\% & 29.818\% & 29.429\% & 25.381\% & 0.78124 \\ DNN [26] & 93.192\% & 50.796\% & 50.335\% & 47.265\% & 0.99223 \\ LSTM [11] & 59.624\% & 15.618\% & 15.87\% & 14.194\% & 0.77945 \\ Transformer [34] & 93.422\% & 53.859\% & 50.672\% & 46.547\% & 0.95346 \\ CNN-LSTM [7] & 99.814\% & 58.8\% & 57.566\% & 57.278\% & 0.99916 \\ XGBoost [12] & 94.166\% & 53.989\% & 54.988\% & 56.987\% & 0.78616 \\ XGB-RF [35] & 95.042\% & 61.145\% & 60.212\% & 62.019\% & 0.81572 \\ ROSE-BOX (Ours) & 99.851\% & 63.563\% & 65.687\% & 64.989\% & 0.99395 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the ROSE-BOX model with other methods for detecting CIC-DDoS2019. Figure 6: Comparison of ROSE-BOX with other models using CIC-IDS2017. Figure 7: Comparison of ROSE-BOX with other models using CSE-CIC-IDS2018. As shown in Table 4, the AUROC of ROSE-BOX is 1.0, indicating that the model effectively distinguishes between normal and anomalous instances, which provides a clear view of classification performance across six classes (Class 0-5) on the CIC-IDS2017 dataset, demonstrating an almost flawless match between true and predicted labels. Table 4 shows the specific detection metrics, where ROSE-BOX achieves an accuracy of 99.991%, a precision of 99.993%, a recall of 99.987%, and an F1 score of 99.989%. These near-perfect metrics confirm that ROSE-BOX consistently outperforms other models across all evaluation criteria. Methods such as KNN, DNN, LSTM, CNN-LSTM, and Transformer struggle with the high dimensionality and complexity of IIoT data. In contrast, XGB-RF (XGBoost-assisted Random Forest for feature selection) has better detection performance than using XGBoost alone, and its detection performance without SMOTE and Bayesian optimization (XGB-RF) is inferior to ROSE-BOX. As shown in Table 5, ROSE-BOX achieves perfect classification, with an AUROC of 1.0, indicating ideal detection performance and showing perfect classification across four classes (Class 0-3) on the CSE-CIC-IDS2018 dataset.