## 5 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **FSACM** & **Naive Bayes** & **Decision Tree** & **Decision Table** & **AdaBoost** \\ \hline
**Tuesday Working Hours** & 99.87 & 97.15 & 99.68 & 99.18 & 98.97 \\ \hline
**Wednesday Working Hours** & 99.63 & 98.8 & 99.49 & 98.86 & 93.51 \\ \hline
**Thursday Working Hours Morning Web Attacks** & 99.52 & 94.89 & 99.14 & 99.16 & 98.34 \\ \hline
**Thursday Working Hours Afternoon Infiltration** & 99.91 & 97.93 & 99.81 & 99.84 & 99.19 \\ \hline
**Friday Working Hours Morning** & 99.88 & 96.46 & 99.83 & 99.82 & 99.23 \\ \hline
**Friday Working Hours Afternoon DDos** & 99.89 & 99.78 & 99.89 & 99.85 & 99.57 \\ \hline
**Friday Working Hours Afternoon PortScan** & 99.90 & 99.59 & 99.87 & 99.81 & 99.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performances of the proposed method on CIC-IDS2017 dataset versus other machine learning algorithms when distinguishing attacks from the normal network traffic. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Technique** & **False Positive** & **Precision** & **F-Measure** & **Accuracy** \\ \hline FSACM & 0.025 & 97.27 & 97.13 & 98.84 \\ \hline Naive Bayes & 0.052 & 80.07 & 74.8 & 71.82 \\ \hline Decision Tree & 0.030 & 85.92 & 85.81 & 86.24 \\ \hline Decision Table & 0.033 & 82.11 & 81.90 & 82.61 \\ \hline SMO & 0.026 & 85.25 & 85.95 & 86.46 \\ \hline AdaBoost & 0.280 & 75.15 & 69.45 & 63.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performances of the proposed method on UNSW-NB15 dataset versus other machine learning algorithms when distinguishing attacks from the normal network traffic. Even though the results of the study by [PERSON] and [PERSON] [44] were evaluated well in terms of the number of features used, the suggested method was outperformed in terms of accuracy. In addition, the previous version of our study results was also better than most of the other studies in terms of accuracy and the number of features that were used. Adding new algorithms as well as updating the existing algorithms in our previous method increased model performances in terms of DR and accuracy while using fewer features in this study. In addition to developed research with machine learning methods, there are many industrial IDS solutions such as Solarwinds, Bro, Suricata, and Snort. A comparison of our proposed new methodology with these solutions is listed below:  
Detection Accuracy: Commercial IDS solutions typically have a large and well-established database of known attack patterns (signatures) and advanced machine learning algorithms to detect anomalies. They often offer high detection accuracy for known attacks. The proposed technique is based on signature and anomaly detection, and may be effective in detecting novel/unknown attacks but might produce false positives. Ease of Deployment: Commercial IDS solutions often come with user-friendly interfaces and setup wizards, making them relatively easy to deploy. The proposed technique, being a research-based algorithm, may require more technical expertise to implement and configure correctly. Maintenance and Updates: Commercial IDS solutions are actively maintained by dedicated security teams. They receive regular updates, including new attack signatures and vulnerability patches. The proposed technique's maintenance and update schedule would depend on the researchers behind it. Cost: Commercial IDS solutions have licensing costs, which can vary widely depending on features and scalability. The proposed technique may be open-source or research-oriented, potentially reducing initial costs. In summary, the proposed novel technique may have certain advantages in detecting novel attacks due to its anomaly-based approach. Still, it lacks some features such as support, updates, and reliability that commercial IDS solutions provide. Additionally, the proposed technique may be combined with other commercial IDS solutions to improve its overall security posture.