## 4 Evaluation
### Comparison with the Latest Techniques  
The researchers proposed some unsupervised intrusion detection algorithms such as RBM [7], PCA [8] and deep auto-encoder [9]. Those algorithms usually ignore the similarity relationship between network packets when preprocessing network packet extraction features. The lack of information analyzed by those algorithms results in relatively low accuracy. The experiment compares the performance of the proposed algorithm with the existing algorithms. Table 14 is a comparison of the performance of the various algorithms. Deep auto-encoder preforms better than PCA and RBM in Table 14, so we use deep auto-encoder to implement the proposal for further improvement. It can be seen from Table 14 that the proposed packet2 vec-AE algorithm achieves the best performances regarding the accuracy exceeding those of the other state-of-the-art algorithms by 11.6%. The proposed packet2 vec-AE algorithm achieves the best performances regarding the precision exceeding those of the other state-of-the-art algorithms by 11.9%. The detection rate was only worse than that of the best algorithm and ranks second among all five algorithms. We used the harmonic mean \(F_{1}\) to comprehensively assess the detection rate and precision. The proposed packet2 vec-AE algorithm \begin{table}
\begin{tabular}{c c} \hline
**Hyper-Parameters** & **Value** \\ \hline probability of returning to the previous node \(p\) & 2 \\ probability of moving away from the source node \(q\) & 0.2 \\ penalty values \(\eta\) & \(-\)0.3 \\ pruning threshold \(\varepsilon\) & 0.86 \\ truncating network packets lengths & 100 \\ length of random work \(l\) & 60 \\ \hline \end{tabular}
\end{table} 
Table 11: List of Parameters for Packet2 vec in Packet2 vec-AE. \begin{table}
\begin{tabular}{c c c c} \hline
**Input** & **ACC** & **DR** & **Precision** & \(\mathbf{F_{1}}\) \\ \hline
10 & 89.8 & 87.9 & 88.0 & 87.9 \\
60 & **94.7** & **90.9** & **94.3** & **92.6** \\
80 & **94.7** & **90.9** & **94.3** & **92.6** \\
100 & 90.2 & 87.3 & 88.7 & 88.0 \\ \hline \end{tabular}
\end{table} 
Table 9: Compare the effects of random walks of different lengths on the performance of Packet2 vec-AE (%). \begin{table}
\begin{tabular}{c c} \hline
**Hyper-Parameters** & **Value** \\ \hline Optimizer & _addelta_ \\ Learning Rate & 0.25 \\ Activation function & _tanh_ \\ \hline \end{tabular}
\end{table} 
Table 12: List of Auto-encoder Hyper-parameters and Its Optimizer in Packet2 vec-AE. \begin{table}
\begin{tabular}{c c c} \hline
**Length \(I\)** & **ACC** & **DR** & **Precision** & \(\mathbf{F_{1}}\) \\ \hline
10 & 89.8 & 87.9 & 88.0 & 87.9 \\
60 & **94.7** & **90.9** & **94.3** & **92.6** \\
80 & **94.7** & **90.9** & **94.3** & **92.6** \\
100 & 90.2 & 87.3 & 88.7 & 88.0 \\ \hline \end{tabular}
\end{table} 
Table 10: Architecture Parameters of auto-encoder in the packet2 vec-AE algorithm. \begin{table}
\begin{tabular}{c c} \hline
**Layer** & **Type** & **Filter/Neuron** \\ \hline
1 & encoder+tanh & 160 \\
2 & encoder+tanh & 64 \\
3 & encoder+tanh & 32 \\
4 & dense & 10 \\
5 & decoder+tanh & 32 \\
6 & decoder+tanh & 64 \\
7 & decoder+tanh & 160 \\ \hline \end{tabular}
\end{table} 
Table 10: Architecture Parameters of auto-encoder in the packet2 vec-AE algorithm. \begin{table}
\begin{tabular}{c c} \hline
**Hyper-Parameters** & **Value** \\ \hline probability of returning to the previous node \(p\) & 2 \\ probability of moving away from the source node \(q\) & 0.2 \\ penalty values \(\eta\) & \(-\)0.3 \\ pruning threshold \(\varepsilon\) & 0.86 \\ truncating network packets lengths & 100 \\ length of random work \(l\) & 60 \\ \hline \end{tabular}
\end{table} 
Table 11: List of Parameters for Packet2 vec in Packet2 vec-AE. achieves the best performances regarding the \(F_{1}\) exceeding those of the other state-of-the-art algorithms by 8.7%. The proposed node2 vec-AE algorithm achieves the good performances regarding the accuracy, the precision, and the \(F_{1}\) exceeding those of the other state-of-the-art algorithms by 4.3%, 4.9%, and 0.2%, respectively.