# 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  
In this section, different dimensionality reduction methods (AE-FS, PCA, LLE, ISOMAP, and autoencoder) are used to reduce the dimensionality of the dataset to 25, 20, 18, and 16 dimensions and then perform model training by comparing the performance of the model in terms of training time, training accuracy, and loss rate. The experimental results are as follows: Figures 7-10 show the training accuracy and loss rate of the model, and Figure 10 shows the training time of the model. In addition, the one-dimensional convolutional neural network classification experiment is carried  
Figure 8: The change in training accuracy and loss rate when the data is downscaled to 20 dimensions. Figure 10: The change in training accuracy and loss rate when the data is downscaled to 16 dimensions. Figure 9: The change in training accuracy and loss rate when the data is downscaled to 18 dimensions. Regarding the training accuracy of the training model, the AE-FS dimensionality reduction dataset shows apparent advantages in the training process. For example, when the dataset is reduced to 25 dimensions, the feature reduction rate is 10.8% (as shown in Figure 7). The AE-FS+TI-CNN model first reaches the fitting state at the beginning of training and reaches the highest at the end of training. The training accuracy rate is about 98%. Similarly, when the dataset is reduced to 20 dimensions, the feature reduction rate is 28.6% (as shown in Figure 8). The FS+TI-CNN model has a clear advantage in training accuracy, which is higher than that of the PCA+TI-CNN model by 2.5%. In addition, when the dataset is reduced to 18 dimensions, the feature reduction rate is 35.7% (as shown in Figure 9), and the training accuracy of the FS+TI-CNN model is 0.8% higher than that of the PCA+TI-CNN model. Finally, when the dataset is reduced to 16 dimensions, the feature reduction rate is about 42.9% (as shown in Figure 10). The AE-FS+TI-CNN model also has a clear advantage in training accuracy. Compared with AUTO+TI-CNN the model is 0.5% higher. In summary, the AE-FS dimensionality reduction method shows excellent performance and advantages when dealing with high-dimensional datasets, and its model quickly reaches the fitting state during the training process and achieves the highest training accuracy at the end of the training. Regarding model training loss rate, when the dataset is reduced to 25 dimensions (as shown in Figure 7), the AE-FS+TI-CNN model performs best, which is 3% lower than the ISOMAP+TI-CNN model training loss rate. Meanwhile, the loss rate of the LLE+TI-CNN model converges the slowest. When the dataset is reduced to 20 dimensions (as shown in Figure 8), the loss rate of the AE-FS+TI-CNN model is 4% lower than that of the ISOMAP+TI-CNN model, and the loss rate of the LLE+TI-CNN model also converges the slowest. When the dataset is reduced to 18 and 16 dimensions (as shown in Figures 9 and 10), the loss rate of the AE-FS+TI-CNN model also performs best. Compared  
Figure 11: Comparison of training time of models with different dimensionality reduction methods. with the PCA+TI-CNN model and AUTO+, the TI-CNN model is about 2.5% and 2% lower, respectively, and the loss rate convergence of the LLE+TI-CNN model is still the slowest. Regarding model training time, the experimental results are shown in Figure 11. In general, the less dimensionality of the dataset, the shorter the training time of the model. When reducing the dataset to 25 dimensions, the AUTO+TI-CNN model exhibits the longest training time of approximately 204.5 s, whereas the AE-FS+TI-CNN model shows the shortest training time of about 179.9 s. The difference between the two is approximately 25 s, with a relative difference rate of around 12.2%.