# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Results and Analysis
#### 4.7.7 Performance by Class
Compared to the **macro averages**, the **weighted averages** emphasize the significance of considering class imbalance when evaluating the model's overall performance. The weighted average takes into account the contribution of each class in proportion to its support, providing a more accurate representation of the model's effectiveness across the entire dataset. In contrast, the macro average treats each class equally, potentially skewing the assessment towards classes with larger supports. The consistently higher weighted averages reflect this model's stronger performance across classes with substantial support, indicating its robustness when handling imbalanced datasets and ability to accurately classify majority classes while maintaining competitive performance in smaller classes. **Class imbalance** significantly affects model performance, as can be seen in the classes with high and low support. Classes with higher support generally perform better due to more data being available for the model to learn from. Conversely, classes with lower support struggle with poor performance metrics, highlighting challenges in capturing the nuanced patterns of less frequent classes. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.0000 & 0.0000 & 0.0000 & 22,766 \\ \hline
1.0 & 0.2090 & 0.2500 & 0.2277 & 1,014,202 \\ \hline
2.0 & 0.0000 & 0.0000 & 0.0000 & 819,011 \\ \hline
3.0 & 0.2850 & 0.8989 & 0.4327 & 2,061,989 \\ \hline
4.0 & 0.2012 & 0.4039 & 0.2686 & 1,550,155 \\ \hline
5.0 & 0.0000 & 0.0000 & 0.0000 & 240,528 \\ \hline
6.0 & 0.0000 & 0.0000 & 0.0000 & 1,031,974 \\ \hline
7.0 & 0.0000 & 0.0000 & 0.0000 & 522,122 \\ \hline
8.0 & 0.0000 & 0.0000 & 0.0000 & 1,400,360 \\ \hline
9.0 & 0.0000 & 0.0000 & 0.0000 & 37,392 \\ \hline
10.0 & 0.9326 & 0.2691 & 0.4177 & 1,294,758 \\ \hline
11.0 & 0.4681 & 0.3360 & 0.3912 & 4,016,516 \\ \hline
12.0 & 0.0000 & 0.0000 & 0.0000 & 73,667 \\ \hline
13.0 & 0.0000 & 0.0000 & 0.0000 & 88 \\ \hline
**Accuracy** & \multicolumn{4}{c}{0.3146 (14,085,528)} \\ \hline
**Macro Avg** & 0.1497 & 0.1541 & 0.1241 & 14,085,528 \\ \hline
**Weighted Avg** & 0.2981 & 0.3146 & 0.2593 & 14,085,528 \\ \hline \end{tabular}
\end{table} 
Table 7: Classification report (1% labeled data). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.0000 & 0.0000 & 0.0000 & 22,766 \\ \hline
1.0 & 0.2005 & 0.1208 & 0.1508 & 1,014,202 \\ \hline
2.0 & 0.00  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0.0 & 0.0000 & 0.0000 & 0.0000 & 22,766 \\ \hline
1.0 & 0.2457 & 0.1364 & 0.1760 & 1,014,202 \\ \hline
2.0 & 0.0  
4.7.13. Co-Training with Random Forest Base Estimator  
The performance of the co-training model with a random forest base estimator was evaluated with varying amounts of labeled data (1%, 5%, 10%, 50%, and 100%). A summary of the results is provided below:  
4.7.14. Key Metrics
* **Accuracy**: Very high across all levels of labeled data
* **1% labeled data**: 140,869 samples
- **5% labeled data**: 140,855 samples
- **10% labeled data**: 140,855 samples
- **50% labeled data**: 140,855 samples
- **100% labeled data**: 140,855 samples  
4.7.15. Performance by Class  
The performance of the proposed method