## Appendix A Metrics of Designed Neural Networks  
We present the accuracy of each neural network intended to be a decision element in Table 3. However, in order to provide a comprehensive information about the process, we provide the other metrics here as an appendix. In the following tables, we present a confusion matrix, precision, recall and F1-scorescore for the best representative of each neural network and each class. The metrics are defined as follows. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\text{,} \tag{1}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\text{,} \tag{2}\]  
\[\text{F1-score}=\frac{2}{\text{Recall}^{-1}+\text{Precision}^{-1}}\text{,} \tag{3}\]  
where TP (true positive) is the number of correctly recognized decisions for each class (ALLOW, BLOC, INSPECTION,...), FP (false positive) is is the number of incorrectly recognized decisions for a specific class and FN (false negative) is the number of decisions wherein the specific class was not recognized. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Alllow** & **Block** & **Forward** & **Inspection** & **QoS\_af13** & **QoS\_af41** & **QoS\_ef** \\  & **Predicted** & **Predicted** & **Predicted** & **Predicted** & **Predicted** & **Predicted** \\ \hline
**Allow Actual** & 3164 & 0 & 16 & 3 & 21 & 1 & 24 \\
**Block Actual** & 31 & 6187 & 0 & 0 & 0 & 0 & 0 \\
**Forward Actual** & 0 & 0 & 3133 & 0 & 1 & 0 & 0 \\
**Inspection** & 0 & 0 & 0 & 3062 & 14 & 0 & 0 \\
**Actual** & & & & & & & \\
**QoS af13 Actual** & 2 & 0 & 2 & 425 & 2059 & 634 & 2 \\
**QoS af41 Actual** & 0 & 0 & 0 & 0 & 15 & 3119 & 0 \\
**QoS ef Actual** & 0 & 0 & 0 & 0 & 1 & 0 & 3084 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix for Net1. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Alllow** & **Block** & **Forward** & **Inspection** & **QoS\_af13** & **QoS\_af41** & **QoS\_ef** \\  & **Predicted** & **Predicted** & **Predicted** & **Predicted** & **Predicted** & **Predicted** & **Predicted** \\ \hline
**Allow Actual** & 3185 & 0 & 11 & 0 & 24 & 0 & 9 \\
**Block Actual** & 33 & 6185 & 0 & 0 & 0 & 0 & 0 \\
**Forward Actual** & 0 & 0 & 3134 & 0 & 0 & 0 \\
**Inspection** & 0 & 0 & 0 & 3076 & 0 & 0 \\
**Actual** & 1 & 0 & 2 & 0 & 3120 & 0 \\
**QoS af13 Actual** & 0 & 0 & 0 & 5 & 3129 & 0 \\
**QoS ef Actual** & 2 & 0 & 0 & 0 & 0 & 3083 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification report for LeNet-5. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Samples** \\ \hline
**Allow** & 0.9909 & 0.9789 & 0.9849 & 3229 \\
**Block** & 1.0000 & 0.9955 & 0.9977 & 6218 \\
**Forward** & 0.9937 & 0.9997 & 0.9967 & 3134 \\
**Inspection** & 0.8423 & 0.9987 & 0.9139 & 3076 \\
**QoS af13** & 0.9816 & 0.5813 & 0.7302 & 3124 \\
**QoS af41** & 0.8099 & 0.9965 & 0.8936 & 3134 \\
**QoS ef** & 0.9904 & 0.9997 & 0.9950 & 3085 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix for AlexNet. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Samples** \\ \hline
**Allow** & 0.9872 & 0.9808 & 0.9840 & 3229 \\
**Block** & 0.9998 & 0.9937 & 0.9968 & 6218 \\
**Forward** & 0.9940 & 0.9987 & 0.9963 & 3134 \\
**Inspection** & 0.8431 & 0.9587 & 0.8972 & 3076 \\
**QoS af13** & 0.8800 & 0.6620 & 0.7556 & 3124 \\
**QoS af41** & 0.8559 & 0.9592 & 0.9046 & 3134 \\
**QoS ef** & 0.9936 & 0.9994 & 0.9964 & 3085 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix for LeNet-5. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Samples** \\ \hline
**Allow** & 0.9935 & 0.9867 & 0.9901 & 3229 \\
**Block** & 0.9994 & 0.9971 & 0.9982 & 6218 \\
**Forward** & 0.9956 & 1.0000 & 0.9978 & 3134 \\
**Inspection** & 0.9968 & 1.0000 & 0.9984 & 3076 \\
**QoS af13** & 0.9923 & 0.9946 & 0.9934 & 3124 \\
**QoS af41** & 0.9997 & 1.0000 & 0.9998 & 3134 \\
**QoS ef** & 0.9974 & 0.9987 & 0.9981 & 3085 \\ \hline \hline \end{tabular}
\end{table}