## 4 Results and Discussion
### Result Analysis  
The confusion matrices of the IMFL-IDSCS model on intrusion classification are demonstrated in Figure 3. The outcomes exhibit that the IMFL-IDSCS approach properly categorized all intrusions and normal samples. With 80% of the training (TR) set, the IMFL-IDSCS model identified 36,302 into DoS, 549 into R2l, 8768 into Probe, 2 into U2R, and 53,414 into normal. Eventually, with 20% of testing (TS) set, the IMFL-IDSCS approach identified 9130 into DoS, 127 into R2l, 2324 into Probe, 3 into U2R, and 13,177 into normal. Meanwhile, with 70% of TR set, the IMFL-IDSCS technique identified 31,838 into DoS, 479 into R2l, 8007 into Probe, 0 into U2R, and 46,461 into normal. Finally, with 30% of the \begin{table}
\begin{tabular}{l l} \hline \hline
**Class** & **No. of Samples** \\ \hline Denial of service attack (Dos) & 45,927 \\ Unauthorized access from a remote host (R2l) & 995 \\ Port monitoring or scanning (Probe) & 11,656 \\ Unauthorized local super user privileged access (U2r) & 52 \\ Not an Attack (Normal) & 67,343 \\ \hline
**Total number of samples** & **125,973** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the dataset. TS set, the IMFL-IDSCS method identified 13,581 into DoS, 212 into R2l, 3407 into Probe, 0 into U2R, and 20,004 into normal. Table 2 and Figure 4 provide the comprehensive intrusion recognition results of the IMFL-IDSCS method on 80% of TR and 20% of TS databases. The figure demonstrates that the IMFL-IDSCS model showed maximum outcome under all classes. For sample, on 80% of the TR database, the IMFL-IDSCS approach provided an average \(accu_{y}\) of 99.31%, \(prec_{n}\) of 77.90%, \(rec_{l}\) of 73.26%, \(F_{score}\) of 73.86%, and Mathew correlation coefficient (MCC) of 74.03%. Moreover, on 20% of the TS database, the IMFL-IDSCS approach rendered an average \(accu_{y}\) of 99.31%, \(prec_{n}\) of 92.03%, \(rec_{l}\) of 78.25%, \(F_{score}\) of 81.80%, and MCC of 82.83%. Table 3 and Figure 5 provide the overall intrusion recognition outcomes of the IMFL-IDSCS model on 70% of TR and 30% of TS databases. The outcomes exhibit that the IMFL-IDSCS method showed maximum outcome under all classes. For the sample, on 70% of the TR database, the IMFL-IDSCS approach presented an average \(accu_{y}\) of 99.37%, \(prec_{n}\) of 75.14%, \(rec_{l}\) of 73.09%, \(F_{score}\) of 74%, and MCC of 73.56%. On 30% of the TS database, the IMFL-IDSCS technique offered an average \(accu_{y}\) of 99.38%, \(prec_{n}\) of 74.26%, \(rec_{l}\) of 72.77%, \(F_{score}\) of 73.45%, and MCC of 73%. The training accuracy (TACC) and validation accuracy (VACC) of the IMFL-IDSCS technique are inspected on ID performance in Figure 6. The figure displays that the IMFL-IDSCS approach showed improved performance with increased values of TACC and VACC. It is seen that the IMFL-IDSCS algorithm reached maximum TACC outcomes. Figure 3: Confusion matrices of IMFL-IDSCS system (**a**,**b**) TR and TS databases of 80:20 and (**c**,**d**) TR and TS databases of 70:30. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & \(Acc_{y}\) & \(Prec_{n}\) & \(Reca_{1}\) & \(F_{score}\) & MCC \\ \hline \multicolumn{5}{l}{**Training Phase (80\%)**} \\ \hline Dos & 98.88 & 98.00 & 98.94 & 98.47 & 97.59 \\ R2l & 99.49 & 67.53 & 68.71 & 68.11 & 67.86 \\ Probe & 99.20 & 96.13 & 95.12 & 95.62 & 95.18 \\ U2r & 99.95 & 28.57 & 04.65 & 08.00 & 11.51 \\ Normal & 99.01 & 99.29 & 98.86 & 99.08 & 98.01 \\ \hline \multicolumn{5}{l}{**Average**} & **99.31** & **77.90** & **73.26** & **73.86** & **74.03** \\ \hline \multicolumn{5}{l}{**Testing Phase (20\%)**} \\ \hline Dos & 98.81 & 97.94 & 98.84 & 98.39 & 97.45 \\ R2l & 99.47 & 66.49 & 64.80 & 65.63 & 65.37 \\ Probe & 99.20 & 96.39 & 95.32 & 95.85 & 95.42 \\ U2r & 99.98 & 100.00 & 33.33 & 50.00 & 57.73 \\ Normal & 99.09 & 99.31 & 98.96 & 99.14 & 98.18 \\ \hline \multicolumn{5}{l}{**Average**} & **99.31** & **92.03** & **78.25** & **81.80** & **82.83** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of IMFL-IDSCS system under 80:20 of TR/TS databases.