## 4 Performance Validation  
This section inspects the experimental validation of the OMLIDS-PBIoT technique using two benchmark datasets such as NSL-KDD and UNSW-NB15 datasets. The FS outcomes of the GEO-FS system on the test dataset are displayed in Table 1. The experimental values implied the GEO-FS method has accomplished enhanced performance over other models. For instance, on test NSL-KDD dataset, the OMLIDS-PBIoT technique has accomplished least good cost of 0.002218 with the selection of 13 features. Additionally, on test UNSW-NB15 dataset, the OMLIDS-PBIoT technique has established minimum best cost of 0.003095 with the selection of 20 features. A detailed intrusion detection of the outcomes of the OMLIDS-PBIoT technique on the NSL-KDD dataset are portrayed in Table 2 and Figure 3. The outcomes denoted by the OMLIDS-PBIoT technique have classified samples under all classes effectively. For example, the OMLIDS-PBIoT technique has recognized samples under DoS attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and \(acc_{y}\) of 99.30%, 99.50%, 99.64%, and 99.57% respectively. Additionally, the OMLIDS-PBIoT technique has recognized samples under R2L attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and \(acc_{y}\) of 99.53%, 98.52%, 99.18%, and 99.21% correspondingly. At the same time, the OMLIDS-PBIoT technique has recognized samples under U2R attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and \(acc_{y}\) of 99.89%, 99.12%, 98.45%, and 99.54% individually. Moreover, the OMLIDS-PBIoT technique has recognized samples under normal attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and \(acc_{y}\) of 98.65%, 98.84%, 98.74%, and 99.07%, correspondingly. The training accuracy (TA) and validation accuracy (VA) attained by the OMLIDS-PBIoT technique on NSL-KDD dataset is demonstrated in Figure 4. The experimental \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack Type** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline DoS & 99.30 & 99.50 & 99.64 & 99.57 \\ \hline R2L & 99.53 & 98.52 & 99.18 & 99.21 \\ \hline Probe & 98.54 & 99.53 & 99.44 & 99.68 \\ \hline U2R & 99.89 & 99.12 & 98.45 & 99.54 \\ \hline Normal & 98.65 & 98.84 & 98.74 & 99.07 \\ \hline Average & 99.18 & 99.10 & 99.09 & 99.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of OMLIDS-PBIoT technique on NSL-KDD dataset. Figure 3: Result analysis of OMLIDS-PBIoT technique under NSL-KDD dataset. outcome implied that the OMLIDS-PBIoT technique has gained maximum values of TA and VA. In specific, the VA seemed to be higher than TA. The training loss (TL) and validation loss (VL) achieved by the OMLIDS-PBIoT technique on NSL-KDD dataset are established in Figure 5. The experimental outcome inferred that the OMLIDS-PBIoT technique has accomplished least values of TL and VL. In specific, the VL seemed to be lower than TL. Figure 4: TA and VA analysis of OMLIDS-PBIoT technique under NSL-KDD dataset. Figure 5: TL and VL analysis of OMLIDS-PBIoT technique under NSL-KDD dataset. A detailed intrusion detection outcome of the OMLIDS-PBIoT technique on the UNSW-NB15 dataset is displayed in Table 3 and Figure 6. The outcomes exposed that the OMLIDS-PBIoT technique has classified samples under all classes effectively. For example, the OMLIDS-PBIoT model has recognized samples under DoS attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and \(acc_{y}\) of 99.69%, 99.33%, 99.54%, and 99.74%, correspondingly. Eventually, the OMLIDS-PBIoT technique has recognized samples under Generic attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and \(acc_{y}\) of 99.58%, 99.78%, 98.78%, and 99.67%, correspondingly. In the meantime, the OMLIDS-PBIoT technique has recognized samples under U2R attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and \(acc_{y}\) of 99.609%, 99.43%, 98.58%, and 99.54%, correspondingly.