# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Results and Analysis
#### 4.7.7 Performance by Class
This poor performance is primarily attributed to its significantly smaller support of only 88 samples. With lower support, the model has less data to learn from, leading to difficulties in accurately capturing the intricate patterns and nuances specific to Class 13. As a result, precision, recall, and F1-score for this class are comparatively lower, indicating a higher rate of misclassifications or difficulties in identifying its instances correctly. Compared to the **macro average**, the **weighted average** emphasizes the significance of considering class imbalances 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 toward classes with larger support. The consistently higher weighted average reflects this model's strong performance across classes with substantial support, indicating robustness in handling imbalanced datasets and accurate classification of majority classes while maintaining competitive performance for smaller classes. 4.7.9. Logistic Regression with Self-Training  
The performance of the logistic regression model was evaluated with varying amounts of labeled data (1%, 5%, 10%, 50%, and 100%). A summary of the results is provided below:  
4.7.10. Key Metrics
* **Accuracy**: Suboptimal across all levels of labeled data  
* **1% labeled data**: 9,654,220 samples
* **5% labeled data**: 9,844,375 samples
* **10% labeled data**: 9,809,161 samples
* **50% labeled data**: 9,559,847 samples
* **100% labeled data**: 8,875,291 samples  
4.7.11. Performance by Class  
The performance of the proposed method is given in Table 7, Table 8, Table 9, Table 10 and Table 11 corresponding to 1%, 5%, 10%, 50% and 100% of the labeled data, respectively. 4.7.12. Highlights  
**Class 0 and Class 2** show very poor performance metrics (precision, recall, F1-score: 0.0000) across all levels of labeled data. These results indicate that the model struggles significantly with these classes, potentially due to inadequate feature representation or insufficient labeled data for these specific classes. **Class 3** shows strong recall across all levels of labeled data, particularly with 10% and higher. This high recall, reaching as high as 95.78% with 10% labeled data, indicates the model's ability to correctly identify the majority of Class 3 instances. However, the precision for this class is moderate, suggesting that while most Class 3 instances are identified, there are still a significant number of false positives. **Class 10** has high precision (around 93%) but relatively low recall (around 28%) across all labeled data levels. This indicates that while the model is very accurate when it predicts Class 10, it misses a significant number of instances, leading to a moderate F1-score. **Class 11** consistently exhibits relatively high performance metrics across all levels of labeled data. With substantial support of 4,016,516 samples, this class shows consistent precision, recall, and F1-score. Although not perfect, the consistent performance suggests that the logistic regression model is particularly adept at 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. This poor performance is primarily attributed to its significantly smaller support of only 88 samples. With lower support, the model has less data to learn from, leading to difficulties in accurately capturing the intricate patterns and nuances specific to Class 13. As a result, precision, recall, and F1-score for this class are 0.0000, indicating a higher rate of misclassifications or difficulties in correctly identifying its instances.