# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 7 Evaluation Metrics  
The datasets used in the different papers analyzed in Section 6, the main outcome for their solution is given in Table 6. The evaluation metrics used in the literature review (Section 6) to assess their solution is given in Table 7. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors** & **Database Used** & **Outcome/Accuracy** \\ \hline [PERSON] et al. [23] & KDDcup99 & NA \\ \hline [PERSON] et al. [20] & KDDcup99 & 95.75\% \\ \hline [PERSON] et al. [19] & KDDcup99 & 99.97\% \\ \hline [PERSON] et al. [32] & KDDcup99 & 94.76\% (KDDcup99) \\  & NSL-KDD & 97.27\% (NSL-KDD) \\  & UNSW-NB15 & 90.47\% (UNSW-NB15) \\  & CICIDS2017 & 90.40\% (CICIDS2017) \\ \hline [PERSON] et al. [35] & KDDcup99 & 97.85\% (KDDcup99) \\  & NSL-KDD & 85.42\% (NSL-KDD) \\ \hline [PERSON] et al. [36] & NSL-KDD & 99.35\% \\ \hline [PERSON] et al. [39] & KDDcup99 & 89.23\% \\ \hline [PERSON] et al. [28] & KDDcup99 & 94\% \\ \hline [PERSON] et al. [29] & Combination of UNSW-NB15 and CICIDS2017 & 99.24\% \\ \hline [PERSON] et al. [31] & NSL-KDD & 85.20\% \\ \hline [PERSON] et al. [33] & NSL-KDD & 82.36\% \\ \hline \end{tabular}
\end{table} 
Table 6: Outcome/Accuracy. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors** & **Advantages** & **Disadvantages** \\ \hline [PERSON] et al. [26] (2021) & An SVM model using Naïve Bayes for feature selection with great accuracy results on two recent datasets UNSW-NB15 and CICIDS2017. & Their solution does not identify what kind of attack is used. \\ \hline [PERSON] et al. [27] (2021) & KNN model using PM-CSCA for optimization that achieves great accuracy on an old dataset NSL-KDD and a recent dataset UNSW-NB15. Their solution is used for wireless network and is based on the cloud to reduce the time and speed required by the algorithm. \\ \hline  & An IDS using a Double-Layered Hybrid Approach (DLHA). They first used Intersectional Correlated Feature Selection (ICFS) and PCA to select important features. They then split the NSL-KDD dataset into two groups, one with all the classes and one with only the U2R, R2L and normal classes. These two groups are used to train the model which is composed of a first layer using Naïve Bayes classifier and a second layer using SVM. Their solution achieved outstanding accuracy for R2L and U2R with respective accuracy of 96.67\% and 100\%. \\ \hline [PERSON] et al. [22] (2022) & A random forest model for a wireless network. & The mean accuracy obtained is 96.93\%. No known datasets were used. \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._ \begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Database Used** & **Outcome/Accuracy** \\ \hline  & KDDcup99 & 93\% (KDDcup99) \\  & NSL-KDD & 79.42\% (NSL-KDD) \\ [PERSON] et al. [34] & Kyoto & 87.78 \% (Kyoto) \\  & UNSW-NB15 & 76.48\% (UNSW-NB15) \\  & CICIDS2017 & 94.5\% (CICIDS2017) \\ \hline [PERSON] et al. [37] & KDDcup99 & 99.996\% (KDDcup99) \\  & UNSW-NB15 & 89.134\% (UNSW-NB15) \\ \hline [PERSON] et al. [40] & NSL-KDD & 99.45\% \\ \hline [PERSON] et al. [18] & CICIDS2017 & 99.56\% \\ \hline [PERSON] et al. [30] & NSL-KDD & 92.34\% (NSL-KDD) \\  & UNSW-NB15 & 92\% (UNSW-NB15) \\ \hline [PERSON] et al. [38] & KDDcup99 & 92.49\% (KDDcup99) \\  & UNSW-NB15 & 93.40\% (UNSW-NB15) \\  & CICIDS2017 & 97.90\% (CICIDS2017) \\ \hline [PERSON] et al. [42] & KDDcup99 & 98.19\% (KDDcup99) \\  & UNSW-NB15 & 80.54\% (UNSW-NB15) \\ \hline [PERSON] et al. [17] & UNSW-NB15 & 95.60\% \\ \hline [PERSON] et al. [21] & CICIDS2017 & 80\% \\ \hline [PERSON] et al. [24] & NSL-KDD & 89.33\% \\ \hline [PERSON] et al. [26] & CICIDS2017 & 98.92\% (CICIDS2017) \\  & UNSW-NB15 & 93.75\% (UNSW-NB15) \\ \hline [PERSON] et al. [27] & NSL-KDD & 99.33\% (NSL-KDD) \\  & UNSW-NB15 & 98.27\% (UNSW-NB15) \\ \hline [PERSON] et al. [41] & NSL-KDD & 93.11\% \\ \hline [PERSON] et al. [22] & NA & 96.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._