# Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
## 5 Results
### Comprehensive Performance Analysis of the Proposed Method  
Tables 5 and 6 present the experimental results conducted to verify the impact of transfer learning in target domains with limited data. To analyze this effect, the accuracy and F1-score of each model were compared as the training data ratio was progressively reduced to 100%, 66%, and 33%. According to the experimental results in Table 5, the proposed model consistently maintains the highest accuracy across all data ratios, with an increasing performance gap between models as the available data decreases. When utilizing 100% of the training data, the proposed model achieved 94.21% accuracy, outperforming all comparison models. At this stage, KNN recorded the second-highest accuracy at 94.12%, while CNN-based models also maintained relatively high accuracy. As the training data ratio decreased to 66%, most existing models experienced a decline in accuracy. However, the proposed model demonstrated superior performance, achieving an accuracy of 94.10%, while the CNN + Attention (TL) model recorded 93.90%, showing the second-highest performance. When the training \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline KNN & 94.12 & 94.05 \\ LR & 90.86 & 90.77 \\ SVM & 92.06 & 91.84 \\ CNN (NoTL) & 92.70 & 92.63 \\ CNN + Attention (NoTL) & 93.71 & 93.64 \\ CNN + Attention (TL) & 93.96 & 93.90 \\ Proposed model & 94.21 & 94.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of categorical classification performance among various models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Train 100\%** & **Train 66\%** & **Train 33\%** \\ \hline KNN & 94.12 & 93.67 & 93.02 \\ LR & 90.86 & 90.74 & 90.41 \\ SVM & 92.06 & 91.82 & 91.38 \\ CNN (NoTL) & 92.70 & 92.40 & 91.38 \\ CNN + Attention (NoTL) & 93.71 & 93.32 & 92.43 \\ CNN + Attention (TL) & 93.96 & 93.77 & 93.08 \\ Proposed model & 94.21 & 94.10 & 93.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy evaluation of models with 100%, 66%, and 33% training data. data ratio was further reduced to 33%, all models exhibited a decrease in accuracy due to the significant reduction in data. However, the proposed model demonstrated the smallest performance degradation. Notably, while the CNN + Attention (NoTL) model showed a significant performance drop to 92.43% and the CNN (NoTL) model decreased to 91.38%, the proposed model exhibited the smallest performance degradation. These results indicate that the proposed model maintains a high level of generalization performance even in limited data environments. Notably, the proposed model demonstrates greater robustness to data reduction than traditional machine learning models (KNN, SVM, LR) and exhibits more effective feature learning and a greater generalization performance than existing CNN- and attention-based models. Next, when comparing F1-scores across models at different training data ratios (100%, 66%, 33%) using Table 6, similarly to the experimental results in Table 5, the proposed model consistently achieved the highest F1-score across all data ratios. Additionally, as the training data decreased, the performance gap between models tended to increase. With 100% of the training data, the proposed model recorded the highest F1-score of 94.16%, demonstrating the best performance. KNN followed with 94.05%, while the CNN + Attention (TL) model maintained competitive performance with an F1-score of 93.90%. When the training data ratio decreased to 66%, most existing models experienced performance degradation, but the proposed model showed the smallest performance decrease with an F1-score of 94.05%. Compared to the CNN + Attention (NoTL) model, which recorded 93.27%, and the CNN (NoTL) model, which dropped to 92.33%, the proposed model demonstrated higher robustness to data reduction. When the training data ratio was reduced to 33%, the F1-score of all models decreased; however, the proposed model maintained the highest F1-score at 93.59%.