## 5 Results and Discussion
Scalability is important, especially in the context of intrusion detection systems for large-scale networks for real-world applications. The computational complexity of the proposed method's algorithms' running time was in the accepted range, so the performance of the proposed method in terms of DR, accuracy, false positives, etc., was not affected significantly when the size of the analyzed data input grew. In addition, proper preprocessing, feature selection, and dimensionality reduction stages were applied to reduce the computational burden of the proposed method while maintaining its effectiveness. However, the proposed method was tested on limited hardware resources including CPU, memory, and storage. Thus, it is not very scalable for large network traffic. We aim to test it on bigger hardware resources for future work to handle larger-scale networks. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Feature Selection** & **Classification Technique** & **Num\_of Features** & **Dataset Rate (\%)** \\ \hline [PERSON] et al., 2012 [45] & Feature removal method gradually & SVM & 19 & KDD ’99 & 98.62 \\ \hline [PERSON] et al., 2013 [46] & Hybrid filtering feature selection & Naive Bayes & 16 & KDD ’99 & 98.28 \\ \hline [PERSON] and [PERSON] 2014 [47] & Standard information gain & Hybrid PSO-SVM Approach & 18 & KDD ’99 & 99.4 \\ \hline [PERSON] and [PERSON] 2015 [44] & Correlation-based method & J48, SVM, and Naive Bayes & 6 & KDD ’99 & 98.88, 95.2, 73.32 \\ \hline [PERSON] and [PERSON] 2016 [48] & Feature correlation algorithms & Several machine learning algorithms & 12 & UNSW-NB15 & 85.56 \\ \hline [PERSON] and [PERSON] 2016 [49] & Ant Colony Optimization-based method & - & 19 & KDD ’99 & 98.9 \\ \hline [PERSON] et al., 2016 [50] & Higher variable importance score & Random Forest & 25 & KDD ’99 & 91.9 \\ \hline [PERSON] and [PERSON] 2017 [43] & GA-LR wrapper approach & C4.5, RF, and NBTree & 18 & KDD ’99 & 99.8, 99.9, 99.85 \\ \hline [PERSON] and [PERSON] 2017 [51] & ARM algorithm & Random Forest & 8 & UNSW-NB15 & 82.99 \\ \hline [PERSON] and [PERSON] 2017 [52] & Information gain and correlation & Based on ANN & 25 & KDD ’99 & 97.91 \\ \hline [PERSON] et al., 2018 [53] & Particle Swarm Optimization algorithm & Hybrid technique: GA and SVM & 16 & KDD ’99 & 96.38 \\ \hline [PERSON] et al., 2018 [54] & Ensemble model & J48 & 35 & KDD ’99 & 84.25 \\ \hline [PERSON] and [PERSON] 2019 [55] & ARM and KfsSubsetEval & Based on ANN & 5 & UNSW-NB15 & 96.00 \\ \hline [PERSON] et al., 2019 [56] & Ranker- and heuristic-based techniques & C4.5 Decision Tree & 27 & KDD ’99 & 92.98 \\ \hline [PERSON] and [PERSON] [57] & Filter- and wrapper-based method with firefly algorithm & C4.5- and Bayesian Network-based & 10 & KDD ’99 & 90.27 \\ \hline [PERSON] et al., 2020 [58] & Differential Evolution Wrapper Feature Selection & Five and binary classification & 9 & KDD ’99 & 80.15, 87.53 \\ \hline [PERSON] and [PERSON] 2020 [59] & XGBoost-based feature selection & Several machine learning algorithms & 19 & UNSW-NB15 & 90.85 \\ \hline [PERSON] et al., 2020 [60] & Correlation-based feature selection & Bagging and AdaBoost classifier & 13 & KDD ’99 & 99.4 \\ \hline [PERSON] et al., 2021 [12] & Bat algorithm & SVM & 25 & KDD ’99 & 94.16 \\ \hline [PERSON] and [PERSON] 2021 [61] & Hybrid methods: filter and wrapper & Several machine learning algorithms & 23 & UNSW-NB15 & 98.42 \\ \hline [PERSON] et al., 2021 [38] & FSAP & SABADT & 17 & KDD ’99 & 99.65 \\ \hline Proposed Method FSACM & FSAP new version & SABADT new version & \(10/11\) & KDD ’99 & 99.89/98.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparing proposed method results with state-of-the-art methods’ results in the literature.