## 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 c} \hline \hline
**ID** & **Paper** & **Targeted Web Attacks** & \begin{tabular}{c} **Classification** \\ **Model** \\ \end{tabular} & **Dataset** & \
\begin{tabular}{c} **Classification** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Performance Metrics** \\ \end{tabular} & **Limitations** \\ \hline \multirow{3}{*}{P560} & [50] & Web attacks hidden in HTTP requests & CNN & CSIC 2010 & binary & -Accuracy. 0.964 & \multirow{3}{*}{-Consider multi-classification technique using the proposed model} \\  & & & & & -FNC 0.9751 & & \\  & & & & & -FNC 0.0268 & & \\  & & & & & -FNC 0.9831 & & \\  & & & & & -AUC 0.9968 & & \\ \hline \multirow{7}{*}{P561} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & private dataset & binary & -Accuracy. 0.975 & \multirow{7}{*}{-Training BL-LSTM models consume time and computational resources} \\  & & & & & -FNC 0.975 & & \\  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \hline \multirow{7}{*}{P562} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} \\  & & & & & & & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \cline{3-7}  & & & & & -FNC 0.975 & & \\ \hline \multirow{7}{*}{P563} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} \\  & & & & & & & & \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & & & & & & & & \\ \hline \multirow{7}{*}{P562} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} \\  & & & & & & & & \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \hline \multirow{7}{*}{P563} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} \\  & & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \hline \multirow{7}{*}{P564} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} \\  & & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \hline \multirow{7}{*}{P565} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} & \multirow{7}{*}{[50]} \\  & & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Due to different performance measures and datasets, it is hard to compare and rank studies. However, we observed that most if not all reviewed studies achieved high-performance metrics, but few of them had discussed the threats to validity of their experiments (i.e., what are the things that may invalidate the results of the experiments).