## 5 Results and Discussion
The performance of the proposed method and machine learning algorithms in classifying attack types can be seen in Table 10. After the attacks were separated from the normal network traffic flows, further classification was performed to find the attack types. As can be seen from Table 10, the proposed method outperformed machine learning classifiers in classifying attack types. For example, when classifying DoS attacks, the proposed method was 98.66%, Naive Bayes 41.32%, Decision Tree 50.79%, Decision Table 46.34%, SMO 51.43%, and AdaBoost 0.01%, respectively. Similar results were obtained when other attack types such as normal and backdoor and analysis were classified. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Technique** & **Normal** & **DoS** & **Probe** & **U2R** & **R2L** \\ \hline FSACM & 99.90 & 99.91 & 98.67 & 82.54 & 97.0 \\ \hline NaiveBayes & 80.41 & 88.31 & 85.21 & 68.32 & 34.84 \\ \hline DecisionTree & 99.83 & 99.89 & 98.53 & 43.47 & 95.41 \\ \hline DecisionTable & 99.59 & 99.83 & 93.10 & 47.82 & 90.32 \\ \hline SMO & 99.46 & 96.79 & 95.12 & 47.82 & 88.08 \\ \hline AdaBoost & 99.05 & 98.55 & 63.41 & 4.34 & 0.02 \\ \hline \end{tabular}
\end{table} 
Table 8: Performance of proposed method as well as ML algorithms when classifying types of attacks. When the proposed method was tested on the CIC-IDS2017 dataset, it was observed that an accuracy rate of 99.52-99.91% was achieved for intrusion detection on each.csv file (Table 11). Other algorithms produced high results but were not as successful as the proposed method. In addition, the success rate varies depending on the type of attack contained in the files and the traces shown by the attacks. More detailed analyses and evaluations will be performed on updated datasets, including the CIC-IDS2017 dataset, in future studies. To illustrate the proposed method's performance effectiveness more clearly, the suggested method was also compared with leading methods in the literature. The results of the comparisons are presented in Table 12. When evaluating the methods that were used in the literature, there were not many studies that focused on developing algorithms for both feature selection as well as attack detection. A few studies in the literature focused on developing algorithms that performed both feature selection as well as attack detection. Although [PERSON] and [PERSON]'s [43] study achieved high accuracy, our proposed method outperformed the presented papers' performances in terms of the utilized features. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Tech.** & **Normal** & **Backd.** & **Anal.** & **Fuzz.** & **Shell.** & **Recon.** & **Exp.** & **DoS** & **Worm** & **Gener.** \\ \hline FSACM & 99.81 & 98.15 & 98.23 & 98.96 & 99.12 & 98.41 & 99.18 & 98.66 & 98.15 & 99.56 \\ \hline N.Bayes & 69.93 & 16.35 & 46.34 & 57.51 & 60.00 & 81.73 & 58.11 & 41.32 & 38.46 & 96.15 \\ \hline D.Tree & 96.81 & 2.88 & 4.64 & 56.93 & 46.96 & 80.7 & 73.38 & 50.79 & 69.23 & 97.94 & **DR(\%)** \\ \hline D.Table & 95.38 & 1.92 & 2.03 & 47.41 & 29.57 & 78.41 & 63.13 & 46.34 & 38.46 & 95.88 \\ \hline SMO & 99.90 & 0.01 & 0.01 & 58.67 & 16.53 & 74.01 & 69.30 & 51.43 & 0.01 & 96.51 \\ \hline AdaB. & 91.59 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 96.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of proposed method as well as ML algorithms when classifying types of attacks.