# AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement
## 4 Experiment and Analysis
### AE-DTI Effectiveness Experiment
#### 4.4.1 Comparison Experiments of Different Dimensionality Reduction Methods
When the dataset is reduced to 20 dimensions, the AUTO+TI-CNN model requires the longest training time, about 169.2 s; the AE-FS+TI-CNN model requires the shortest training time, about 142.3 s. Similarly, when the dataset is reduced to 18 dimensions, the AUTO+TI-CNN model requires the longest training time, about 143.9 s, and the AE-FS+TI-CNN model requires the shortest training time, about 128 s. Furthermore, when the dataset is reduced to 16 dimensions, the training time required for the AUTO+TI-CNN model is the shortest, which is 115.8 s. Therefore, it can be concluded that the AE-FS dimensionality reduction method can significantly reduce the training time of the model while maintaining a high training accuracy. From Table 4, we implemented five different dimensionality reduction techniques to decrease the number of dimensions in the dataset to 25, 20, 18, and 16. The dimension re-duction time for LLE and ISOMAP is considerably longer compared to PCA, AUTO, and AE_FS. This is because LLE and ISOMAP take into account more local information and the relationships between data points during the calculation process, resulting in increased dimension reduction time. AE_FS takes slightly longer than PCA and AUTO for data dimensionality reduction, but it outperforms them in terms of model training time and classification accuracy. It is important to note that the data dimensionality reduction process can be performed offline, which helps mitigate the impact of dimensionality re-duction time on the overall model performance. Therefore, considering data dimension reduction time, model training time, and classification accuracy, it is evident that AE-FSS still holds significant advantages over other methods. In practical applications, AE-FSS offers the dual advantages of efficiency and accuracy in data processing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Data** \\ **Dimension** \\ \end{tabular} & **Method** & \
\begin{tabular}{c} **Data Reduction** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Model Training** \\ **Time (s)** \\ \end{tabular} & **Accuracy** \\ \hline \multirow{4}{*}{25} & PCA & 3.2 & 185.7 & 97.2\% \\  & LLE & 111.4 & 186.6 & 96.7\% \\  & ISOMAP & 155.6 & 194.6 & 97.0\% \\  & AUTO & 6.2 & 204.5 & 97.1\% \\  & AE\_FS & 10.1 & 179.9 & 98.8\% \\ \hline \multirow{4}{*}{20} & PCA & 3.2 & 145.8 & 97.1\% \\  & LLE & 111.6 & 148.8 & 95.3\% \\  & ISOMAP & 157.2 & 149.1 & 96.8\% \\  & AUTO & 5.7 & 169.2 & 97.2\% \\  & AE\_FS & 10.3 & 142.3 & 98.6\% \\ \hline \multirow{4}{*}{18} & PCA & 3.2 & 136.4 & 96.9\% \\  & LLE & 113.4 & 143.5 & 94.9\% \\  & ISOMAP & 158.0 & 138.8 & 96.5\% \\  & AUTO & 5.7 & 143.9 & 97.8\% \\  & AE\_FS & 9.8 & 128 & 98.5\% \\ \hline \multirow{4}{*}{16} & PCA & 3.2 & 133.9 & 96.5\% \\  & LLE & 113.1 & 121.1 & 94.5\% \\ \cline{1-1}  & ISOMAP & 156.6 & 128.2 & 96.7\% \\ \cline{1-1}  & AUTO & 5.6 & 116.6 & 97.7\% \\ \cline{1-1}  & AE\_FS & 10.1 & 115.8 & 98.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comprehensive comparison of different dimensionality reduction methods. According to the results in Figure 12, it can be observed that the training time of the model is significantly reduced through the AE-FS dimensionality reduction dataset. Especially when the dataset is reduced to 16 dimensions, the time reduction is the most significant, with a drop of up to 54.3%. At the same time, the classification accuracy of the AE-FS+TI-CNN model is the same as that of the original dataset, the highest training accuracy is 98.8%, and the lowest is 98.3%. Compared with the original dataset. The classification accuracy of the AE-FS+TI-CNN model increased by about 0.7% and 0.2%. Therefore, it shows that dimensionality reduction through AE-FS not only fully preserves the key features of the dataset, but also dramatically reduces the training time of the model.