## 5 Experimental Results
### Comparison with Different Systems without Feature Filtration  
To assess the improvement in ID better, the performance of the proposed IDS was compared with the performance levels of the three IDSs applied to all 41 features of the KDD99 dataset, as illustrated in Table 4. The comparison reveals that the proposed IDS exhibits moderate performance in comparison with CNN BiLSTM, IGP-BP, and Group CNN. The comparison results highlight the need for the modification and improvement of this system by applying feature filtration to discard unimportant features that deteriorate the ID performance of this system. In terms of the FAR, the comparison results indicate that both the CNN BiLSTM and the proposed IDS require improvement to reduce their FAR values. Table 5 compares the performance of the proposed IDS after applying FLAME and the XCS to other IDSs when tested on the KDD99 dataset. The values of the ID performance metrics demonstrate that the proposed IDS outperforms all the previous IDSs. However, its performance was similar to that of RFS-1 and TSLD in terms of the AC which reaches to 100% of accuracy. Also, the proposed work stated at the top rank among listed methods in terms of the DR. Furthermore, the proposed work gained the third-best FAR. Thus, the proposed work gains remarkable achievements in terms of AC, the DR and the FAR. From another perspective, the number of features of the IDSs listed in Table 5 was either equal to or lower than the number of features employed in the proposed IDS, except for [PERSON] and [PERSON] [15], who extracted 29, not 20, features. In summary, Tables 4 and 5 confirm the importance of feature filtration, regardless of the method used to specify the features that increase the AC, the DR and reduce the FAR. Furthermore, there is no specific set of features to be used in all IDSs, and there is always a need to determine the best combination of features for each new IDS. Figures 5 and 6 reveal the results shown in Table 5. Figure 5 shows a comparative analysis of accuracy among all different methods that applied feature filtration. The  
Figure 4: ID accuracy with no feature filtration. \begin{table}
\begin{tabular}{c c c c c c} \hline
**System** & **Year** & **DR** & **AC\%** & **FAR** & **\# Features** \\ \hline Proposed IDS & 2023 & 100 & 99.99 & 0.05 & 20 \\ \hline HYFSPSO [32] & 2023 & NA & 99.53 & NA & \textless{}41 \\ \hline VDCNN-GRU [30] & 2023 & NA & 98.73 & NA & \textless{}41 \\ \hline NIDS-CNNLSTM [52] & 2023 & NA & 97.05 & 0.59 & \textless{}41 \\ \hline EFS-DNN [11] & 2022 & NA & 99.92 & 0.09 & 14 \\ \hline RSA [23] & 2022 & 92.344 & 44.34 & NA & 10 \\ \hline RFS-1 [21] & 2021 & 99.99 & 99.99 & 0.01 & 16 \\ \hline Cuckoo-ID [13] & 2020 & 98.9 & NA & 0.09 & 19 \\ \hline FGLCC [16] & 2019 & 95.23 & 95.03 & 1.65 & \textless{}15 \\ \hline DNN [19] & 2019 & NA & 93.2 & \textgreater{}0 & 8 \\ \hline TSLD [26] & 2019 & NA & 99.99 & 0.0001 & 10 \\ \hline SVM-IDS [31] & 2019 & NA & 98.95 & NA & 20 \\ \hline AE-CNN [27] & 2019 & NA & 93.99 & 6.82 & \textless{}41 \\ \hline Sasan and Sharma [15] & 2016 & NA & 88.23 & NA & 29 \\ \hline LSSVM-IDS + FMIFS [51] & 2016 & 98.93 & 99.94 & 0.13 & 19 \\ \hline DBN-LR [24] & 2016 & NA & 97.9 & 0.5 & 5 \\ \hline NDAE-RF [25] & 2015 & NA & 97.85 & 2.15 & 22 \\ \hline LSTM [28] & 2015 & NA & 96.93 & 10.04 & 8 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison between systems in intrusion detection performance after feature filtration. accuracy of the proposed IDS reaches with 99.99% that reside in top rank among other methods. Also, Figure 6 shows a comparative analysis of the FAR among different methods. The proposed IDS has the third-lowest received FAR with 0.05 and TSLD has the lowest received FAR. This indicate the outperform result of the proposed IDS in terms of the FAR compared with other methods.