## 5 Experiment Results and Discussions
### The Detection Performance of the Hybrid Approach  
In this section, we present the detection performance of the hybrid approach and compare its results with K-NN and variational autoencoder. In our offline case study, we conducted 31 experiments that includes \(SYN,SARFU,IP\)-\(Spoofing,ICMP,andSARFU\) attacks and their combinations. During our experiments, we divide the attacks into different sets. For instance, given each set, we assume that its attacks are known to the trained model and the remaining attacks are unknown. For example, considering \(W\) as a set consists of {IP-Spoofing, SARFU, ICMP} as known attacks, whereas UDP and SYN attacks are considered unknown attacks.  
In each experiment, we report the average precision, recall, and F1-score among all 10-fold supervised approaches (K-NN), variational autoencoder, and the hybrid approach. Table 6 reports the results of the experiments in the physical and simulated environments. We observe that the hybrid approach always significantly outperforms the K-NN approach in terms of recall and F1-score. In terms of precision, the K-NN result is higher. However, it is important to note that it achieves higher precision due to its poor recall, i.e., it detects a smaller number of attacks, but more precisely. In comparison with the variational autoencoder, the hybrid approach achieves a similar or improved performance in terms of precision, recall, and F1-score.  
Figure 8: Variational autoencoder robustness experiment results on simulation environment.  
Figure 7: Variational autoencoder robustness experiment results on physical environment.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Physical Environment**} & \multicolumn{6}{c}{**Simulated Environment**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} \\ \hline
**Variational Autoencoder** & \multicolumn{2}{c}{**0.86**} & \multicolumn{2}{c}{**0.93**} & \multicolumn{2}{c}{**0.90**} & \multicolumn{2}{c}{**0.85**} & \multicolumn{2}{c}{**0.97**} & \multicolumn{2}{c}{**0.91**} \\ \hline
**Known Attacks** & **K-NN** & **Hybrid** & **K-NN** & **Hybrid** & **K-NN** & **Hybrid** & **K-NN** & **Hybrid** & **K-NN** & **Hybrid** & **K-NN** & **Hybrid** \\ \hline (ICMP) & 1.00 & 0.86 & 0.16 & 0.94 & 0.28 & 0.90 & 1.00 & 0.90 & 0.83 & 0.99 & 0.91 & 0.94 \\ (IP-Spoofing) & 0.99 & 0.87 & 0.11 & 0.97 & 0.20 & 0.91 & 0.97 & 0.90 & 0.61 & 0.98 & 0.75 & 0.94 \\ (IP-Spoofing,ICMP) & 0.99 & 0.87 & 0.20 & 0.96 & 0.33 & 0.91 & 0.98 & 0.50 & 0.92 & 1.00 & 0.95 & 0.95 \\ (IP-Spoofing,SARFU) & 1.00 & 0.87 & 0.35 & 0.96 & 0.52 & 0.91 & 0.99 & 0.90 & 0.88 & 0.99 & 0.93 & 0.94 \\ (IP-Spoofing,SARFU,ICMP) & 1.00 & 0.87 & 0.43 & 0.96 & 0.60 & 0.91 & 0.99 & 0.91 & 0.90 & 0.98 & 0.94 & 0.94 \\ (SARFU) & 1.00 & 0.87 & 0.23 & 0.94 & 0.37 & 0.90 & 1.00 & 0.90 & 0.82 & 0.97 & 0.90 & 0.94 \\ (SARFU) & 1.00 & 0.86 & 0.40 & 0.94 & 0.57 & 0.90 & 1.00 & 0.91 & 0.86 & 0.99 & 0.93 & 0.94 \\ (SYN) & 1.00 & 0.86 & 0.02 & 0.93 & 0.04 & 0.90 & 1.00 & 0.90 & 0.80 & 0.99 & 0.89 & 0.94 \\ (SYNLCMP) & 1.00 & 0.87 & 0.31 & 0.93 & 0.47 & 0.90 & 1.00 & 0.90 & 0.84 & 0.98 & 0.91 & 0.94 \\ (SYN-IP-Spoofing,ICMP) & 0.99 & 0.87 & 0.30 & 0.97 & 0.47 & 0.92 & 0.98 & 0.90 & 0.90 & 1.00 & 0.94 & 0.95 \\ (SYNLP-Spoofing,ICMP) & 0.99 & 0.87 & 0.38 & 0.97 & 0.55 & 0.92 & 0.98 & 0.90 & 0.93 & 1.00 & 0.95 & 0.95 \\ (SYNSARFU) & 1.00 & 0.86 & 0.25 & 0.94 & 0.41 & 0.90 & 1.00 & 0.90 & 0.85 & 1.00 & 0.92 & 0.95 \\ (SYNSARFU,ICMP) & 1.00 & 0.