## 4 Results and Discussion
With 70% of TR dataset, the SFSA-DLIDS methodology recognized 33,748 samples into normal class and 34,669 samples into anomaly class. Moreover, with 30% of TS dataset, the SFSA-DLIDS approach recognized 14,607 samples into normal class and 14,752 samples into anomaly class. Along with that, with 20% of TS dataset, the SFSA-DLIDS method recognized 10,026 samples into normal class and 9839 samples into anomaly class. Table 3 and Figure 8 show the overall classification output of the SFSA-DLIDS technique on the test CICIDS 2017 dataset. The results portrayed that the SFSA-DLIDS approach resulted to improvised results under all aspects. For example, with 70% of TR data, the 3-DLIDS method rendered average \(accu_{y}\) of 98.45%, \(prec_{n}\) of 98.51%, \(rec_{q}\) of 98.39%, and \(F_{score}\) of 98.44%. In the meantime, with 30% of TS data, the SFSA-DLIDS technique presented average \(accu_{y}\) of 98.46%, \(prec_{n}\) of 98.52%, \(rec_{q}\) of 98.39%, and \(F_{score}\) of 98.45%. Figure 7: Confusion matrices of SFSA-DLIDS approach under CICIDS 2017 dataset: (**a**) 70% of TR data, (**b**) 30% of TS data, (**c**) 80% of TR data, and (**d**) 20% of TS data. Simultaneously, with 20% of TS data, the SFSA-DLIDS approach offered average \(accu_{y}\) of 99.44%, \(prec_{n}\) of 99.44%, \(rec_{n}\) of 99.44%, and \(F_{score}\) of 99.44%. Figure 8: Average analysis of SFSA-DLIDS approach under CICIDS 2017 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multicolumn{5}{c}{**Training Phase (70\%)**} \\ \hline Normal & 98.45 & 97.79 & 99.35 & 98.56 \\ \hline Anomaly & 98.45 & 99.24 & 97.42 & 98.32 \\ \hline
**Average** & **98.45** & **98.51** & **98.39** & **98.44** \\ \hline \multicolumn{5}{c}{**Testing Phase (30\%)**} \\ \hline Normal & 98.46 & 97.79 & 99.37 & 98.57 \\ \hline Anomaly & 98.46 & 99.26 & 97.40 & 98.32 \\ \hline
**Average** & **98.46** & **98.52** & **98.39** & **98.45** \\ \hline \multicolumn{5}{c}{**Training Phase (80\%)**} \\ \hline Normal & 99.48 & 99.49 & 99.54 & 99.52 \\ \hline Anomaly & 99.48 & 99.47 & 99.42 & 99.44 \\ \hline
**Average** & **99.48** & **99.48** & **99.48** & **99.48** \\ \hline \multicolumn{5}{c}{**Testing Phase (20\%)**} \\ \hline Normal & 99.44 & 99.47 & 99.49 & 99.48 \\ \hline Anomaly & 99.44 & 99.41 & 99.40 & 99.40 \\ \hline
**Average** & **99.44** & **99.44** & **99.44** & **99.44** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result analysis of SFSA-DLIDS approach with various measures on CICIDS 2017 dataset. The TA and VA attained by the SFSA-DLIDS algorithm on CICIDS 2017 dataset are demonstrated in Figure 9. The experimental outcome shows the SFSA-DLIDS algorithm gained higher values of TA and VA. To be specific, the VA is higher than TA. The TL and VL acquired by the SFSA-DLIDS technique on CICIDS 2017 dataset are exhibited in Figure 10. The experimental outcome denoted the SFSA-DLIDS approach accomplished minimal values of TL and VL. Particularly, the VL is lesser than TL. Figure 10: TL and VL analysis of SFSA-DLIDS approach under CICIDS 2017 dataset. Figure 9: TA and VA analysis of SFSA-DLIDS approach under CICIDS 2017 dataset. For ensuring the enhanced performance of the SFSA-DLIDS model, a comparative examination is made in Table 4[3, 13]. The results implied that the WISARD, Forest-PA, and LIB-SVM models have obtained lower \(accu_{y}\) values of 96.22%, 96.53%, and 96.56% respectively. Followed by the GSAE and AE-RF models which attained slightly enhanced \(accu_{y}\) values of 97.44% and 97.55%, respectively. Though the FURIA model resulted in reasonable \(accu_{y}\) of 98.82%, the SFSA-DLIDS model accomplished maximum \(accu_{y}\) of 99.44%. From the detailed results and discussion, it is obvious that the SFSA-DLIDS model has shown enhanced security in the CPS environment.