## 4 Performance Validation
Figure 5: TLOS and VLOS outcome of XAIID-SCPS system on NSLKDD2015 dataset. The confusion matrices of the XAIID-SCPS methodology in the CICIDS 2017 dataset are shown in Figure 6. The results recognized the normal and abnormal samples proficiently. For instance, with 80% of TRS, the XAIID-SCPS technique identifies 39,508 normal and 39,422 abnormal samples. Along with that, with 20% of TSS, the XAIID-SCPS technique identifies 9774 normal and 9950 abnormal samples. Finally, with 30% of TSS, the XAIID-SCPS technique identifies 14,582 normal and 14,505 abnormal samples. In Table 3, a brief intrusion recognition outcome of the XAIID-SCPS method on the CICIDS 2017 dataset is provided under 70:30 of TRS/TSS. The experimental results demonstrated that the proposed model properly recognizes normal and anomaly samples under 70:30 of TRS/TSS. With 80% of TRS, the XAIID-SCPS technique obtains an average \(accu_{bal}\) of 98.66%, \(prec_{n}\) of 98.66%, \(rec_{1}\) of 98.66%, \(F_{score}\) of 98.66%, and \(AUC_{score}\) of 98.66%. Meanwhile, with 20% of TSS, the XAIID-SCPS technique obtains an average \(accu_{bal}\) of 98.62%, \(prec_{n}\) of 98.62%, \(rec_{1}\) of 98.62%, \(F_{score}\) of 98.62%, and \(AUC_{score}\) of 98.62%. Moreover, for 30% of TSS, the XAIID-SCPS technique obtains an average \(accu_{bal}\) of 96.96%, \(prec_{n}\) of 96.96%, \(rec_{1}\) of 96.96%, \(F_{score}\) of 96.96%, and \(AUC_{score}\) of 96.96%. Figure 6: Confusion matrices of XAIID-SCPS system on CICIDS 2017 dataset **(a,b)** TRS/TSS of 80:20 and **(c,d)** TRS/TSS of 70:30. The TACY and VACY of the XAIID-SCPS model on the CICIDS 2017 database are represented in Figure 7. The figure shows that the XAIID-SCPS method has shown enhanced performance with increased values of TACY and VACY. It is visible that the XAIID-SCPS model has higher TACY outcomes. The TLOS and VLOS of the XAIID-SCPS model on the CICIDS 2017 dataset are represented in Figure 8. The figure indicated that the XAIID-SCPS model has better performance with the least values of TLOS and VLOS. It is visible that the XAIID-SCPS model has resulted in reduced VLOS outcomes. In Table 4 and Figure 9, comparative results of the XAIID-SCPS technique with existing models take place [27]. The results indicate that the XAIID-SCPS method shows maximum performance over other existing models. Based on \(accu_{y}\), the XAIID-SCPS technique results in an increasing \(accu_{y}\) of 98.87% while the FURIA, AE-RF, Forest-PA, WIZARD, GSAE, and LIB-SVM models reach a reducing \(accu_{y}\) of 98.14%, 97.62%, 96.72%, 96.64%, 97.63%, and 96.57%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline XAIID-SCPS & 98.87 & 98.95 & 98.87 & 98.91 \\ \hline FURIA & 98.14 & 97.57 & 96.93 & 98.26 \\ \hline AE-RF & 97.62 & 97.35 & 97.79 & 97.30 \\ \hline Forest-PA & 96.72 & 96.97 & 97.32 & 98.13 \\ \hline WISARD & 96.64 & 97.58 & 97.29 & 98.65 \\ \hline GSAE & 97.63 & 95.97 & 98.39 & 98.19 \\ \hline LIB-SVM & 96.57 & 96.96 & 96.83 & 97.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative outcome of XAIID-SCPS system with other systems. Figure 8: TLOS and VLOS outcome of XAIID-SCPS system on CICIDS 2017 dataset. Meanwhile, based on \(prec_{n}\), the XAIID-SCPS method results in an increasing \(prec_{n}\) of 98.95% while the FURIA, AE-RF, Forest-PA, WISARD, GSAE, and LIB-SVM models reach a reducing \(prec_{n}\) of 97.57%, 97.35%, 96.97%, 97.58%, 95.97%, and 96.96%, respectively. Eventually, based on \(F_{score}\), the XAIID-SCPS technique results in an increasing \(F_{score}\) of 98.91% while the FURIA, AE-RF, Forest-PA, WISARD, GSAE, and LIB-SVM models reach a reducing \(F_{score}\) of 98.14%, 98.26%, 97.30%, 98.13%, 98.65%, 98.19%, and 97.92%, correspondingly. These results highlighted the betterment of the XAIID-SCPS technique for intrusion detection purposes.