# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 4 Experimental Results
### Performance Comparison with Existing CNN-Based IDS Models  
To compare the performance of our model with the latest IDS models based on CNN architectures, we investigated many related studies with similar approaches. Table 12 shows the performance comparison results of the latest models and our model with similar approaches. Despite being an outdated network traffic dataset, the NSL-KDD dataset is still widely used for the performance evaluation of deep learning-based IDSs. In addition, many studies have utilized CNN architectures as classification models, and research on hybrid detection models that combine two or more deep learning models is also actively being conducted. As shown in Table 12, the binary classification accuracy results mentioned in the previous experimental results were presented as performance comparison criteria. Our model's performance (99.06% accuracy with a standard CNN) exceeded that of most CNN-based IDS models proposed in previous studies. Exceptions include the models by [PERSON] et al. [18] (99.81%) and [PERSON] et al. [46] (99.50%), which reported slightly higher classification accuracy, with differences of only 0.75% and 0.44%, respectively. [PERSON] et al. [20] also reported a comparable accuracy of 99.13% using a more complex CNN (VGG16) architecture. These minor differences highlight the competitiveness of our streamlined approach. A key strength of our model becomes evident when considering other performance indicators and model complexity. For instance, while [PERSON] et al. [46] achieved a marginally higher accuracy, the other evaluation indicators reported in their study, such as precision (0.98), recall (0.97), and F1-score (0.97), were lower than our model's corresponding scores of 0.9875, 0.9930, and 0.9902 (as detailed in our Section 4.3, Table 9). This suggests our model provides a better balance in identifying true positives while minimizing false alarms. The model by [PERSON] et al. [18] achieved the highest accuracy in Table 12 by employing a hybrid (CNN + GRU) architecture. However, as a general principle, combining two or more deep learning models typically increases the number of neural network layers and parameters, which often leads to increased training time and memory usage. Thus, a potential trade-off for the higher accuracy achieved by such hybrid models is a reduction in computational efficiency. In this respect, while the model [18] showed high classification accuracy, its complex hybrid structure implies it may not be as resource-efficient as our single, comparatively simpler CNN architecture. Similarly, the CNN (VGG16) model used by [PERSON] et al. [20], while achieving comparable accuracy, is known to be a deeper and more computationally intensive architecture than the standard CNN developed in our work. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Dataset** & **Classification Algorithm** & **Accuracy (\%)** \\ \hline Ours & 2024 & NSL-KDD & CNN & 99.06 \\ [PERSON] et al. [53] & 2023 & NSL-KDD & Hybrid (CNN + LSTM) & 99.00 \\ [PERSON] et al. [20] & 2023 & NSL-KDD & CNN (VGG16) & 99.13 \\ [PERSON] et al. [18] & 2022 & NSL-KDD & Hybrid (CNN + GRU) & 99.81 \\ [PERSON] et al. [47] & 2022 & NSL-KDD & Hybrid (CNN + LSTM) & 82.60 \\ [PERSON] et al. [19] & 2022 & NSL-KDD & Hybrid (CNN + LSTM) & 85.59 \\ [PERSON] et al. [50] & 2022 & NSL-KDD & Hybrid (CNN + BiLSTM) & 82.60 \\ [PERSON] et al. [46] & 2021 & NSL-KDD & CNN & 99.50 \\ [PERSON] et al. [17] & 2021 & NSL-KDD & CNN & 90.14 \\ [PERSON] et al. [16] & 2019 & NSL-KDD & CNN & 97.70 \\ [PERSON] et al. [13] & 2019 & NSL-KDD & CNN & 83.31 \\ [PERSON] et al. [14] & 2019 & NSL-KDD & CNN & 86.95 \\ \hline \hline \end{tabular} _Abbreviations: CNN (Convolutional Neural Network); LSTM (Long Short-Term Memory); VGG16 (Visual Geometry Group-16); GRU (Gated Recurrent Unit); BiLSTM (Bidirectional Long Short-Term Memory)._  
\end{table} 
Table 12: The performance comparison with other CNN-based IDS models.