## 4 Results and Discussion
The PODCNN-LWID algorithm identifies 344 samples in class 1; 340 samples in class 2; 350 samples in class 3; 341 samples in class 4; 339 samples in class 5; 336 samples in class 6; 351 samples in class 7; 338 samples in class 8; 322 samples in class 9; 335 samples in class 10; and 338 samples in class 11. Table 3 portrays the intrusion classification output of the PODCNN-LWID technique for 70% of the TR dataset. For class 1, the PODCNN-LWID algorithm offers \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.74%, 98.57%, 98.57%, 99.86%, and 98.57%, respectively. In the meantime, for class 2, the PODCNN-LWID approach grants \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.61%, 99.71%, 96.05%, 99.97%, and 97.84%, respectively. In addition, for class 3, the PODCNN-LWID algorithm renders \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.79%, 100%, 97.77%, 100%, and 98.87%, respectively. Additionally, for class 4, the PODCNN-LWID technique offers \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.71%,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Entire Dataset**} \\ \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & _F-Score_ \\ \hline
1 & 99.75 & 98.60 & 98.60 & 99.86 & 98.60 \\
2 & 99.62 & 99.59 & 96.20 & 99.96 & 97.86 \\
3 & 99.82 & 100.00 & 98.00 & 100.00 & 98.99 \\
4 & 99.67 & 99.59 & 96.80 & 99.96 & 98.17 \\
5 & 99.67 & 99.79 & 96.60 &99.71%, 97.15%, 99.97%, and 98.41%, respectively. Lastly, for class 5, the PODCNN-LWID approach renders \(accu_{y}\), \(prec_{n}\), \(recal_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.61%, 99.71%, 96.03%, 99.97%, and 97.84%, respectively. Figure 5 exemplifies the confusion matrix presented by the PODCNN-LWID approach with 30% of the TS data. The PODCNN-LWID technique identifies 149 samples in class 1; 141 samples in class 2; 140 samples in class 3; 143 samples in class 4; 144 samples in class 5; 149 samples in class 6; 139 samples in class 7; 146 samples in class 8; 148 samples in class 9; 158 samples in class 10; and 154 samples in class 11. Table 4 shows the intrusion classification output of the PODCNN-LWID approach with 30% of the TS dataset. For class 1, the PODCNN-LWID methodology presents \(accu_{y}\), \(prec_{n}\)  
Figure 5: Confusion matrix of PODCNN-LWID approach with 30% of TS data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Training Set (70\%)**} \\ \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline
1 & 99.74 & 98.57 & 98.57 & 99.86 & 98.57 \\
2 & 99.61 & 99.71 & 96.05 & 99.97 & 97.84 \\
3 & 99.79 & 100.00 & 97.77 & 100  
\(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.76%, 98.68%, 98.68%, 99.87%, and 98.68%, respectively. In the meantime, for class 2, the PODCNN-LWID algorithm renders \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.64%, 99.30%, 96.58%, 99.93%, and 97.92%, respectively. Similarly, for class 3, the PODCNN-LWID technique produces \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.88%, 100%, 98.59%, 100%, and 99.29% respectively. Further, for class 4, the PODCNN-LWID technique offer \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.58%, 99.31%, 95.97%, 99.93%, and 97.61%, respectively. Finally, for class 5, the PODCNN-LWID approach produces \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), and \(F_{score}\) values of 99.82%, 100%, 97.96%, 100%, and 98.97%, respectively. The training accuracy (TRA) and validation accuracy (VLA) gained by the PODCNN-LWID approach with the test dataset is shown in Figure 6. The experimental outcome implies that the PODCNN-LWID methodology attains maximal TRA and VLA values. The VLA is greater than the TRA.