# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 5 Results of Attack-Specific Two-Class Models
### Exposure to Unseen Data
#### 5.2.5 DDoS Part 2  
The second day of DDoS traffic in CSE-CIC-IDS2018 has very similar traffic. Only one new tool is introduced, and behind the scenes, it generates requests with the same protocol. It is odd to split the DDoS traffic over two days, because as the results will show, performance is alike.  
Single decision trees have zones with adequate performance that are interwoven with zones with very poor performance metrics. It is not exclusively due to overfitting either, because regions with good performance do exist at higher training volumes. Adaboost models only work with normalized features, maximally reaching perfect precision and 80%  
Figure 7: A rare occurrence of perfect consistency by pretrained IDS2017 DDoS models the respective IDS2018 DDoS samples. (**a**) Singular decision trees within CIC-IDS2017 DDoS; and (**b**) the same set of models summarized in figure (**a**) when evaluating CSE-CIC-IDS2018 DDoS part 1.  
recall. As a standalone result, this would make adaboost a viable option, but once again, the unpredictability with regard to training volume hampers viability. The pretrained bagging classifiers perform like adaboost at low to very low training volumes and exclusively with normalized features, however, with even less stability. Conclusions for randomized trees, random forests and normal gradient-boosted trees can be summarized as lackluster across all parameters (again with an exception for very low training volumes). Regularized gradient-boosted trees perform stably with 80% recall and 100% precision insensitive to feature reduction.  
Nearest neighbors has the same result as in the previous section, but with a worse precision (70+%). HOIC combined with LOIC UDP seems to be harder to classify, because the centroid suffers from very low (<25%) recall compared to the previous section (regardless of parameter selection).  
Both methods of feature scaling obtain good results for the logistic regression models, with those trained on min-max-scaled features reaching clusters of perfect metrics. The best scores are obtained at the lowest training volumes, but the differential is tiny in most cases (0.5%). The linear kernel SVM has the same performance profile as binlr, with good results for the models trained on normalized features, but better results on models trained with min-max-scaled features. Once more, the highest performance is obtained with the lowest amounts of training data. The ideal classifier for this attack class is the RBF-kernel SVM with stable, perfect scores. This does require at least 1% data to train on, but shows no signs of overfitting (min-max-scaling).  
It could be concluded that ML-based models are able to distinguish well between regular and multiple types of DDoS traffic. Unfortunately, due to the loud nature of DDoS attacks, they are easily detectable by other mechanisms. It might be useful at an aggregate level (service providers), but an individual business suffering from a DDoS attack will not need a machine learning model to confirm that.