# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Results and Analysis
#### 4.7.7 Performance by Class  
The performance of the proposed method is given in Table 2, Table 3, Table 4, Table 5 and Table 6 corresponding to 1%, 5%, 10%, 50% and 100% of the labeled data, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.9787 & 0.9687 & 0.9737 & 22,766 \\ \hline
1.0 & 0.9996 & 0.9995 & 0.9995 & 1,014,202 \\ \hline
2.0 & 0.9997 & 0.9998 & 0.9998 & 819,011 \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.9957 & 0.9939 & 0.9948 & 22,766 \\ \hline
1.0 & 0.9999 & 1.0000 & 1.0000 & 1,014,202 \\ \hline
2.0 & 1.0000 & 1.0000 & 1.0000 & 819,011 \\ \hline
3.0 & 1.0000 & 1.0000 & 1.0000 & 2,061,989 \\ \hline
4.0 & 0.9999 & 1.0000 & 1.0000 & 1,550,155 \\ \hline
5.0 & 0.9999 & 1.0000 & 1.0000 & 240,528 \\ \hline
6.0 & 0.9999 & 1.0000 & 1.0000 & 1,031,974 \\ \hline
7.0 & 1.0000 & 0.9999 & 1.0000 & 522,122 \\ \hline
8.0 & 1.0000 & 1.0000 & 1.0000 & 1,400,360 \\ \hline
9.0 & 0.9992 & 0.9994 & 0.9993 & 37,392 \\ \hline
10.0 & 1.0000 & 1.0000 & 1.0000 & 1,294,758 \\ \hline
11.0 & 1.0000 & 1.0000 & 1.0000 & 4,016,516 \\ \hline
12.0 & 0.9994 & 0.9995 & 0.9994 & 73,667 \\ \hline
13.0 & 0.9322 & 0.9659 & 0.9487 & 88 \\ \hline
**Accuracy** & \multicolumn{4}{c}{0.9999 (14,085,528)} \\ \hline
**Macro Avg** & 0.9975 & 0.9983 & 0.9979 & 14,085,528 \\ \hline
**Weighted Avg** & 0.9999 & 0.9999 & 0.9999 & 14,085,528 \\ \hline \end{tabular}
\end{table} 
Table 4: Classification report (10% labeled data). \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.9931 & 0.9881 & 0.9906 & 22,766 \\ \hline \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.9993 & 0.9994 & 0.9993 & 22,766 \\ \hline
1.0 & 1.0000 & 1.0000 & 1.0000 & 1,014,202 \\ \hline
2.0 & 1.0000 & 1.0000 & 1.0000 & 819,011 \\ \hline
3.0 & 1.0000 & 1.0000 & 1.0000 & 2,061,989 \\ \hline
4.0 & 1.0000 & 1.0000 & 1.0000 & 1,550,155 \\ \hline
5.0 & 1.0000 & 1.0000 & 1.0000 & 240,528 \\ \hline
6.0 & 1.0000 & 1.0000 & 1.0000 & 4,016,516 \\ \hline
12.0 & 0.9999 & 0.9999 & 0.9999 & 73,667 \\ \hline
13.0 & 0.9647 & 0.9886 & 0.9765 & 88 \\ \hline
**Accuracy** & \multicolumn{3}{c}{0.999999 (14,085,528)} \\ \hline
**Macro Avg** & 0.9987 & 0.9990 & 0.9988 & 14,085,528 \\ \hline
**Weighted Avg** & 1.0000 & 1.0000 & 1.0000 & 14,085,528 \\ \hline \end{tabular}
\end{table} 
Table 6: Classification report (100% labeled data). \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.9987 & 0.9987 & 0.9987 & 22,766 \\ \hline
1.0 & 1.0000 & 1.0000 & 1.0000 & 1,014,202 \\ \hline
2.0 & 1.0000 & 1.0000 & 1.0000 & 819,011 \\ \hline
3.0 & 1.0000 & 1.0000 & 1.0000 & 2,061,989 \\ \hline
4.0 & 1.0000 & 1.0000 & 1.0000 & 1,550,155 \\ \hline
5.0 & 1.0000 & 1.0000 & 1.0000 & 240,528 \\ \hline
6.0 & 1.0000 & 1.0000 & 1.0000 & 1,031,974 \\ \hline
7.0 & 1.0000 & 1.0000 & 1.0000 & 522,122 \\ \hline
8.0 & 1.0000 & 1.0000 & 1.0000 & 14,00,360 \\ \hline
9.0 & 0.9999 & 0.9999 & 0.9988 & 14,085,528 \\ \hline
**Macro Avg** & 1.0000 & 1.0000 & 1.0000 & 14,085,528 \\ \hline \end{tabular}
\end{table} 
Table 5: Classification report (50% labeled data). 4.7.8. Highlights  
**Class 11** consistently exhibits the highest performance metrics across all levels of labeled data. With a substantial support of 4,016,516 samples, this class demonstrates near-perfect precision, recall, and F1-score. This consistently high performance suggests that the decision tree model is particularly adept at accurately classifying instances belonging to Class 11, indicating robust representation of its distinguishing features within the dataset. **Class 13** presents a contrasting scenario, with the lowest performance metrics among all classes.