## 4 Results and Discussion
### RQ8: What Is the Performance of DL-Based Web Attacks Detection Models?
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **Paper** & **Targetted Web Attacks** & **Classification Model** & **Dataset** & **Classification Type** & **Performance Metrics** & **Limitations** \\ \hline \multirow{9}{*}{PS37} & \multirow{9}{*}{[47]} & \multirow{9}{*}{[47]} & \multirow{9}{*}{[47]} & \multirow{9}{*}{[47]} & -Accuracy 0.8924 & \multirow{9}{*}{[47]} \\  & & & & & -Precision 0.8158 & \\  & & & & -71-score 0.853 & & -Extract effective features \\  & & & & -71-score 0.894 & & -71-score 0.891 & \\  & & & & -AUC 0.936 & & \\ \cline{3-6}  & & & & -Accuracy 0.867 & & \\  & & & & -Precision 0.8109 & & \\  & & & & -71-score 0.813 & & -Post the WAF as cloud service \\  & & & & & -71-score 0.894 & \\  & & & & & -AUC 0.94 & \\ \cline{3-6}  & & & & & -Accuracy 0.878 & \\  & & & & -Precision 0.824 & & \\  & & & & -71-score 0.863 & & \\  & & & & & -71-score 0.817 & \\  & & & & & -AUC 0.92 & \\ \cline{3-6}  & & & & & -Accuracy 0.8435 & \\  & & & & & -Precision 0.8086 & \\  & & & & & -71-score 0.889 & \\  & & & & & -71-score 0.895 & \\  & & & & & -71-score 0.878 & \\  & & & & & -AUC 0.94 & \\ \hline \multirow{9}{*}{PS38} & \multirow{9}{*}{[49]} & \multirow{9}{*}{[47]} & \multirow{9}{*}{[47]} & \multirow{9}{*}{[47]} & \multirow{9}{*}{[47]} & -Accuracy 0.998 & \multirow{9}{*}{[47]} \\  & & & & & -F1-score 0.991 & \\ \cline{3-6}  & & & & & -71-score 0.892 & \\ \cline{3-6}  & & & & & -71-score 0.892 & \\ \cline{3-6}  & & & & & -71-score 0.892 & \\ \cline{3-6}  & & & & & -71-score 0.893 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \cline{3-6}  & & & & & -71-score 0.896 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Paper** & **Targetted Web Attacks** & **Classification Model** & **Dataset** & **Classification Type** & **Performance Metrics** & **Limitations** \\ \hline PS39 & [51] & Malicious [S code] & Bi-directional LSTM & Private dataset & binary & -Accuracy 0.9771 & -Static analysis cannot detect malicious IS code & -generated dynamically \\  & & & & & -F1-score 0.9868 & -Experiment more complex neural networks \\ \cline{5-7}  & & & & & -TPR 0.979 & -Experiment more complex neural networks \\ \cline{5-7}  & & & & & -Precision 1 (Benig) & -TPR 0.99 (Benig) & \\  & & & & & -PRR 0.02 (Benig) & \\  & & & & & -Precision 1 (Directory Traversal (DT)) & \\  & & & & & -TPR 1.0 (DT) & \\ P540 & [53] & -851 & CNN & Private dataset & multi-class & -Precision 0.999 (Greene File & -Test the model performance in more practical \\  & & & & & -TPR 1.0 (BT) & application \\  & & & & & -TPR 1.0 (SQU) & \\  & & & & & -Precision 1.058) & \\  & & & & & -TPR1.0 (SQS) & \\ \hline PS41 & [55] & XSS & DFN & Private dataset & binary & -Accuracy 0.9932 & -Precision 0.9921 & -Precision 0.987 & -Deeply the proposed model in a real-time \\  & & & & & -TPR 0.88 & -Detection system \\  & & & & & -AUC 0.99 & -AUC 0.99 & \\ \hline  & & & & & -Accuracy 0.9971 & -Precision 0.9964 & -Exploit model uncertainty in other security \\  & & & & & -TPR 0.9921 & - scenarios \\  & & & & & -TPR 0.9879 & - \\ PS42 & [57] & Web attacks injected in web logs and HTTP requests & CNN & CSSC 2010 & binary & -Accuracy 0.9851 & -Combine softmax output and the model \\  & & & & & -P1-score 0.987 & - uncertainty as a unified standard to evaluate \\  & & & & & -TPR 0.9921 & - the prediction confidence \\ \cline{5-7}  & & & & -Accuracy 0.9959 & -Precision 0.9978 & \\  & & & & -P1-score 0.991 & -TPR 0.9984 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**ID** & **Paper** & **Targeted Web Attacks** & \ \begin{tabular}{} **Classification** \\ **Model** \\ \end{tabular}  & **Dataset** &