## 4 Results and Discussion  
The experimental validation of the SFSA-DLIDS model is tested using two benchmark datasets, namely, NSLKDD 2015 [27] and CICIDS 2017 [28] datasets. Table 1 illustrates the details on two benchmark datasets. The NSLKDD 2015 dataset holds samples under two classes. It includes 67,343 samples under normal class and 58,630 samples under anomaly class. In addition, the CICIDS 2017 dataset holds 50,000 samples under normal class and 50,000 samples under anomaly class. Figure 3 indicates the confusion matrices produced by the SFSA-DLIDS approach on the test NSLKDD 2015 dataset. With 70% of training (TR) dataset, the SFSA-DLIDS model has recognized 46,762 samples into normal class and 40,054 samples into anomaly class. In addition, with 30% of the testing (TS) dataset, the SFSA-DLIDS method has recognized 20,147 samples into normal class and 17,062 samples into anomaly class. Additionally, with 20% of TS dataset, the SFSA-DLIDS approach has identified 13,390 samples into normal class and 11,665 samples into anomaly class. Table 2 and Figure 4 showcase the overall classification output of the SFSA-DLIDS model on the test NSLKDD 2015 dataset. The results implied that the SFSA-DLIDS model has resulted to enhanced results under all aspects. For instance, with 70% of TR data, the SFSA-DLIDS model has offered average \(accu_{y}\) of 97.74%, \(prec_{n}\) of 97.76%, \(rec_{n}\) of 97.74%, and \(F_{score}\) of 97.74%. Simultaneously, with 30% of TS data, the SFSA-DLIDS approach has rendered average \(accu_{y}\) of 97.86%, \(prec_{n}\) of 97.88%, \(rec_{l}\) of 97.87%, and \(F_{score}\) of 97.86%. Concurrently, with 20% of TS data, the SFSA-DLIDS method has provided average \(accu_{y}\) of 99.32%, \(prec_{n}\) of 99.32%, \(rec_{n}\) of 99.33%, and \(F_{score}\) of 99.32%. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**No. of Samples**} \\ \cline{2-3}  & **NSLKDD 2015** & **CICIDS 2017** \\ \hline Normal & 67,343 & 50,000 \\ \hline Anomaly & 58,630 & 50,000 \\ \hline Total & 125,973 & 100,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. The training accuracy (TA) and validation accuracy (VA) acquired by the SFSA-DLIDS approach on NSLKDD 2015 dataset is demonstrated in Figure 5. The experimental outcome denoted that the SFSA-DLIDS algorithm attained maximal values of TA and VA. In specific, the VA is higher than TA. Figure 3: Confusion matrices of SFSA-DLIDS approach under NSLKDD 2015 dataset: (**a**) 70% of TR data, (**b**) 30% of TS data, (**c**) 80% of TR data, and (**d**) 20% of TS data. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multicolumn{5}{c}{**Training Phase (70\%)**} \\ \hline Normal & 97.74 & 98.76 & 96.67 & 97.71 \\ \hline Anomaly & 97.74 & 96.76 & 98.80 & 97.77 \\ \hline
**Average** & **97.74** & **97.76** & **97.74** & **97.74** \\ \hline \multicolumn{5}{c}{**Testing Phase (30\%)**} \\ \hline Normal & 97.86 & 98.94 & 96.79 & 97.85 \\ \hline Anomaly & 97.86 & 96.82 & 98.95 & 97.87 \\ \hline
**Average** & **97.86** & **97.88** & **97.87** & **97.86** \\ \hline \multicolumn{5}{c}{**Training Phase (80\%)**} \\ \hline Normal & 99.35 & 99.34 & 99.36 & 99.35 \\ \hline Anomaly & 99.35 & 99.37 & 99.34 & 99.35 \\ \hline
**Average** & **99.35** & **99.35** & **99.35** & **99.35** \\ \hline \multicolumn{5}{c}{**Testing Phase (20\%)**} \\ \hline Normal & 99.32 & 99.39 & 99.28 & 99.33 \\ \hline Anomaly & 99.32 & 99.26 & 99.37 & 99.32 \\ \hline
**Average** & **99.32** & **99.32** & **99.33** & **99.32** \\ \hline \end{tabular}
\end{table} 
Table 2: Result analysis of SFSA-DLIDS approach with various measures on NSLKDD 2015 dataset. Figure 4: Average analysis of SFSA-DLIDS approach under NSLKDD 2015 dataset. Figure 7 represents the confusion matrices generated by the SFSA-DLIDS algorithm on the test CICIDS 2017 dataset.