## 4 Performance Validation  
In this section, the intrusion detection results of the XAIID-SCPS method can be investigated on two datasets namely NSLKDD2015 and CICIDS2017 dataset as shown in Table 1.  
The confusion matrices of the XAIID-SCPS method on the NSLKDD 2015 dataset are illustrated in Figure 3. The results recognized the normal and abnormal samples proficiently. For instance, with 80% of TRS, the XAIID-SCPS technique identifies 53,611 normal and 46,054 abnormal samples. Along with that, with 20% of TSS, the XAIID-SCPS technique identifies 13,343 normal and 11,578 abnormal samples. Finally, with 30% of TSS, the XAIID-SCPS technique identifies 19,787 normal and 17,364 abnormal samples. In Table 2, detailed intrusion recognition outcomes of the XAIID-SCPS method on the NSLKDD2015 dataset are provided under 80:20 of TRS/TSS. The experimental results demonstrated that the proposed model properly recognizes normal and anomaly samples under 80:20 of TRS/TSS. With 80% of TRS, the XAIID-SCPS technique obtains an average \(accu_{bal}\) of 98.86%, \(prec_{n}\) of 98.93%, \(rec_{1}\) of 98.86%, \(F_{score}\) of 98.89%, and \(AUC_{score}\) of 98.86%. Meanwhile, with 20% of TSS, the XAIID-SCPS methodology acquires an average \(accu_{bal}\) of 98.87%, \(prec_{n}\) of 98.95%, \(rec_{1}\) of 98.87%, \(F_{score}\) of 98.91%, and \(AUC_{score}\) of 98.87%. Additionally, for 30% of TSS, the XAIID-SCPS method obtains an average \(accu_{bal}\) of 98.32%, \(prec_{n}\) of 98.28%, \(rec_{1}\) of 98.32%, \(F_{score}\) of 98.30%, and \(AUC_{score}\) of 98.32%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**No. of Samples**} \\ \cline{2-5}  & **NSLKDD 2015** & **CICIDS 2017** \\ \hline Normal & 67,343 & 50,000 \\ \hline Anomaly & 58,630 & 50,000 \\ \hline
**Total Number of Samples** & **125,973** & **100,000** \\ \hline \end{tabular}
\end{table} 
Table 1: Details dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**NSLKDD 2015 Dataset**} \\ \hline
**Class** & \(Accu_{bal}\) & \(Prec_{n}\) & \(Reca_{1}\) & \(F_{score}\) & \(AUC_{score}\) \\ \hline \multicolumn{5}{c}{**Training Phase (80\%)**} \\ \hline Normal & 99.40 & 98.55 & 99.40 & 98.97 & 98.86 \\ \hline Anomaly & 98.31 & 99.30 & 98.31 & 98.81 & 98.86 \\ \hline
**Average** & **98.86** & **98.93** & **98.86** & **98.89** & **98.86** \\ \hline \multicolumn{5}{c}{**Testing Phase (20\%)**} \\ \hline Normal & 99.51 & 98.47 & 99.51 & 98.98 & 98.87 \\ \hline Anomaly & 98.24 & 99.43 & 98.24 & 98.83 & 98.87 \\ \hline
**Average** & **98.87** & **98.95** & **98.87** & **98.91** & **98.87** \\ \hline \multicolumn{5}{c}{**Training Phase (70\%)**} \\ \hline Normal & 98.04 & 98.70 & 98.04 & 98.37 & 98.28 \\ \hline Anomaly & 98.52 & 97.76 & 98.52 & 98.14 & 98.28 \\ \hline
**Average** & **98.28** & **98.23** & **98.28** & **98.25** & **98.28** \\ \hline \multicolumn{5}{c}{**Testing Phase (30\%)**} \\ \hline Normal & 98.11 & 98.70 & 98.11 & 98.41 & 98.32 \\ \hline Anomaly & 98.52 & 97.85 & 98.52 & 98.19 & 98.32 \\ \hline
**Average** & **98.32** & **98.28** & **98.32** & **98.30** & **98.32** \\ \hline \end{tabular}
\end{table} 
Table 2: Intrusion recognition outcome of XAIID-SCPS system on the NSLKDD2015 dataset. The TACY and VACY of the XAIID-SCPS model on the NSLKDD2015 dataset are represented in Figure 4. The figure designated the XAIID-SCPS model has shown enhanced performance with maximal values of TACY and VACY. It is visible that the XAIID-SCPS model has maximum TACY results. Figure 3: Confusion matrices of XAIID-SCPS system on NSLKDD2015 dataset (**a,b**) TRS/TSS of 80:20 and (**c,d**) TRS/TSS of 70:30. The TLOS and VLOS of the XAIID-SCPS model on the NSLKDD2015 dataset are represented in Figure 5. The figure inferred that the XAIID-SCPS model has superior performance with least values of TLOS and VLOS. It is visible that the XAIID-SCPS model has resulted in reduced VLOS outcomes. Figure 4: TACY and VACY outcome of XAIID-SCPS system on NSLKDD2015 dataset.