# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 4 Experimental Results
### Comparative Analysis of Feature Correlation-Based and Sequential Pixel Mapping  
This section explains the reasons for employing a method of sequentially representing each feature value at individual pixels in the image generation process. As mentioned in Section 3.3.4, CNN models perform computations considering spatial correlations, specifically the adjacency of pixels during image processing. To examine the impact of such correlations on image generation, we conducted comparative experiments between correlation-based pixel mapping and sequential pixel mapping strategies.  
Figure 6 illustrates the comparison between models trained on images with consideration of feature correlation and models trained without such consideration. The blue curve represents the model trained with correlated features, while the red curve denotes the model trained without considering feature correlation. Solid lines in the graph indicate results from the training set, whereas dashed lines represent results from the validation set. Analysis of the accuracy graph (Figure 6a) and the loss graph (Figure 6b) suggests that feature correlation does not significantly impact model performance. As evidenced by the measured values in Table 8, the differences were found to be minimal. More specifically, the model trained with correlated features achieved an accuracy and validation accuracy of 99.59% and 99.09%, respectively. In contrast, the model without correlation consideration achieved 99.44% accuracy and 98.98% validation accuracy. The difference between these models is merely 0.15% for training accuracy and 0.11% for validation accuracy. Similarly, the difference in loss metrics was negligible (0.0028 for training loss and 0.0024 for validation loss). These minimal differences fall within typical statistical variance ranges for neural network training, indicating no practically significant advantage for correlation-based pixel arrangement. As shown in Figure 6a,b, both models converge to similar performance levels after approximately 40 epochs, with the correlation-based model showing only marginally better results. This marginal performance difference results from two key factors. First, the information gain-based feature selection prioritized individually discriminative features, reducing the additional benefits of correlation-based spatial arrangements. Second, the convolution operations of CNN effectively capture feature interactions regardless of pixel positioning within our compact 6 \(\times\) 6 image representation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & OS & Windows 11 \\  & CPU & Intel Core i7-11700 \\  & RAM & 32 GB \\  & GPU & NVIDIA GTX 1660 Ti \\  & CUDA Toolkit & NVIDIA CUDA Toolkit 12.2 \\  & cuDNN & NVIDIA cuDNN 12.1.66 \\ \hline  & anaconda & 4.12.0 \\  & python & 3.9.0 \\  & jupyter Lab & 4.0.3 \\  & pandas & 1.5.3 \\  & tensorflow & 2.10.1 \\  & keras & 2.10.0 \\  & wandb & 0.15.5 \\  & scikit-learn & 1.2.1 \\  & weka & 3.8.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental environment.  
This experiment verifies that feature correlation has a minimal effect on the classification accuracy performance of the model. Therefore, an image generation method was employed that sequentially maps each feature to a single pixel, prioritizing simplicity and computational efficiency. This choice provides equivalent classification performance while significantly reducing the computational overhead associated with measuring inter-feature correlations and subsequent correlation-based pixel mapping during preprocessing.