# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Results and Analysis
#### 4.7.16. Highlights  
**Class 9** shows the lowest performance metrics across all levels of labeled data, with precision at 1.00 but recall and F1-score at 0.07 and 0.14, respectively. This indicates that while the model is very confident when it does predict Class 9, it rarely identifies instances of this class correctly, leading to high misclassification rates.  
**Classes 0, 3, 5, 10, and 11** show exceptionally high performance across all levels of labeled data, with precision, recall, and F1-scores close to or at 1.00. This indicates that the model is highly effective at identifying and correctly classifying instances of these classes.  
**Class 6** has high precision and F1-score but slightly lower recall (0.95) compared to the best-performing classes. This suggests that the model is highly accurate when predicting Class 6 but occasionally misses some instances.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.99 & 1.00 & 1.00 & 22,766 \\ \hline
1.0 & 0.99 & 1.00 & 0.99 & 1,014,202 \\ \hline
2.0 & 0.99 & 1.00 & 0.99 & 819,011 \\ \hline
3.0 & 0.99 & 1.00 & 1.00 & 2,061,989 \\ \hline
4.0 & 0.96 & 0.99 & 0.97 & 1,550,155 \\ \hline
5.0 & 1.00 & 1.00 & 1.00 & 240,528 \\ \hline
6.0 & 0.99 & 0.95 & 0.97 & 1,031,974 \\ \hline
7.0 & 0.91 & 0.99 & 0.95 & 522,122 \\ \hline
8.0 & 0.99 & 0.96 & 0.98 & 1,400,360 \\ \hline
9.0 & 1.00 & 0.07 & 0.14 & 37,392 \\ \hline
10.0 & 1.00 & 1.00 & 1.00 & 1,294,758 \\ \hline
11.0 & 1.00 & 1.00 & 1.00 & 4,016,516 \\ \hline
12.0 & 1.00 & 0.89 & 0.94 & 73,667 \\ \hline
13.0 & 1.00 & 0.92 & 0.96 & 88 \\ \hline
**Accuracy** & \multicolumn{4}{c}{0.9900 (14,085,528)} \\ \hline
**Macro Avg** & 0.99 & 0.91 & 0.92 & 14,085,528 \\ \hline
**Weighted Avg** & 0.99 & 0.99 & 0.99 & 14,085,528 \\ \hline \end{tabular}
\end{table}
Table 15: Classification report (100% labeled data).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
8.0 & 0.99 & 0.96 & 0.98 & 1,400,360 \\ \hline
9.0 & 1.00 & 0.07 & 0.14 & 37,392 \\ \hline
10.0 & 1.00 & 1.00 & 1.00 & 1,294,758 \\ \hline
11.0 & 1.00 & 1.00 & 1.00 & 4,016,516 \\ \hline
12.0 & 1.00 & 0.89 & 0.94 & 73,667 \\ \hline
13.0 & 1.00 & 0.92 & 0.96 & 88 \\ \hline
**Accuracy** & \multicolumn{4}{c}{0.9900 (14,085,528)} \\ \hline
**Macro Avg** & 0.99 & 0.91 & 0.92 & 14,085,528 \\ \hline
**Weighted Avg** & 0.99 & 0.99 & 0.99 & 14,085,528 \\ \hline \end{tabular}
\end{table}
Table 14: _Cont._  
**Class 7** shows strong performance in terms of both precision (0.91) and recall (0.99), leading to a high F1-score (0.95). This indicates robust ability to correctly identify instances of this class, with a few false positives.  
**Class 12** presents good performance, with precision and F1-score close to 1.00 but slightly lower recall (0.89). This shows that the model generally performs well on this class, but misses a few instances.  
**Class 13** demonstrates lower performance metrics, with precision at 1.00 but recall at 0.83 and F1-score at 0.91. This is likely due to its very small support (88 samples), and indicates challenges in accurately identifying this class due to limited data.  
Compared to the **macro averages**, the **weighted averages** emphasize the importance of considering class imbalances in model evaluation. The weighted averages show consistently higher values, reflecting this model's effectiveness in handling larger classes and maintaining high overall performance despite imbalances.  
**Class imbalance** significantly affects this model's performance, as evidenced by the high performance metrics in classes with large support and lower metrics in classes with small support. This highlights the need for strategies to handle class imbalance in order to improve performance in underrepresented classes.