## 4 Performance Validation
Furthermore, the OMLIDS-PBIoT technique has recognized samples under normal attack with \(prec_{n}\), \(rec_{a}\), \(F1_{score}\), and  
The TA and VA gained by the OMLIDS-PBIoT technique on UNSW-NB15 dataset are shown in Figure 7. The experimental outcome implied that the OMLIDS-PBIoT technique has gained maximal values of TA and VA. Particularly, the VA seemed to be higher than TA. The TL and VL achieved by the OMLIDS-PBIoT techniques on UNSW-NB15 dataset are established in Figure 8. The experimental outcome inferred that the OMLIDS-PBIoT technique has accomplished the least values of TL and VL. Specifically, the VL seemed to be less than TL. Figure 8: TL and VL analysis of OMLIDS-PBIoT technique under UNSW-NB15 dataset. Figure 7: TA and VA analysis of OMLIDS-PBIoT technique under UNSW-NB15 dataset. Table 4 portrays an extensive comparison study of the OMLIDS-PBIoT technique with recent models in terms of different measures [18]. Figure 9 offers a comparative \(prec_{n}\) and \(reca_{l}\) examination of the OMLIDS-PBIoT technique with existing models on test data. The results implied that the AKNN-IDS model has obtained lower \(prec_{n}\) and \(reca_{l}\) values of 0.9219 and 0.9376, respectively. Meanwhile, the DPCDBN-IDS and DT-IDS models have shown slightly improved values of \(prec_{n}\) and \(reca_{l}\). Moreover, the PTDSAE-IDS, AB-IDS, and RF-IDS models have obtained moderately closer values of \(prec_{n}\) and \(reca_{l}\). In line with this, the DBN-IDS, LSTM-IDS, and RNN-IDS models have accomplished reasonable \(prec_{n}\) and \(reca_{l}\) values. Though the AIMMFIDS model has shown near-optimal performance with \(prec_{n}\) and \(reca_{l}\) of 0.9946 and 0.9907, the presented OMLIDS-PBIoT technique has accomplished higher \(prec_{n}\) and \(reca_{l}\) of 0.9962 and 0.9950, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline OMLIDS-PBIoT & 0.9962 & 0.9950 & 0.9931 & 0.9960 \\ \hline AIMMFIDS & 0.9946 & 0.9907 & 0.9898 & 0.9936 \\ \hline DBN-IDS & 0.9867 & 0.9883 & 0.9897 & 0.9874 \\ \hline LSTM-IDS & 0.9845 & 0.9857 & 0.9891 & 0.9863 \\ \hline RNN-IDS & 0.9823 & 0.9868 & 0.9875 & 0.9852 \\ \hline PTDSAE-IDS & 0.9791 & 0.9865 & 0.9860 & 0.9849 \\ \hline DPCDBN-IDS & 0.9512 & 0.9499 & 0.9508 & 0.9498 \\ \hline AKNN-IDS & 0.9219 & 0.9376 & 0.9292 & 0.9199 \\ \hline DT-IDS & 0.9659 & 0.9284 & 0.9542 & 0.9365 \\ \hline AB-IDS & 0.9742 & 0.9321 & 0.9568 & 0.9587 \\ \hline RF-IDS & 0.9756 & 0.9384 & 0.9592 & 0.9598 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of OMLIDS-PBIoT method with existing approaches. Figure 9: \(prec_{n}\) and \(reca_{l}\) analysis of OMLIDS-PBIoT technique with existing approaches. Figure 10 provides a comparative \(F1_{score}\) and \(accu_{y}\) inspection of the OMLIDS-PBIoT technique with existing techniques on test data. The results implied that the AKNN-IDS methodology has obtained lower \(F1_{score}\) and \(accu_{y}\) values of 0.9292 and 0.9199, respectively. In the meantime, the DPCDBN-IDS and DT-IDS methodologies have shown slightly improved values of \(F1_{score}\) and \(accu_{y}\). Next, the PTDSAE-IDS, AB-IDS, and RF-IDS algorithms have gained moderately closer values of \(F1_{score}\) and \(accu_{y}\). In accordance with this, the DBN-IDS, LSTM-IDS, and RNN-IDS methods have accomplished reasonable \(F1_{score}\) and \(accu_{y}\) values. Even though the AIMMFIDS system has displayed near-optimal performance with \(F1_{score}\) and \(accu_{y}\) of 0.9898 and 0.9936, the presented OMLIDS-PBIoT technique has accomplished higher \(F1_{score}\) and \(accu_{y}\) of 0.9931 and 0.9960, correspondingly. Finally, a detailed computation time inspection of the OMLIDS-PBIoT technique with recent models takes place in Table 5. The results implied that the AKNN-IDS, DT-IDS, AB-IDS, and RF-IDS, models have shown the least performance with maximum values of TRT.