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7.3.3 Models In the paper we have analyzed two main models: VAE and C-VAE. In this case, we have also produced several performance metrics: Accuracy, Precision, Recall and F1. Again, the F1 score has been considered the most important due to unbalanced nature of the dataset. All results provided in the paper are obtained using exclusively the NSL-KDD test subset. 7.3.4 Results/Conclusions The results from the paper allow us to conclude that a C-VAE model is applicable to the prediction of intrusions in data networks with better results than other classic models (SVM, Random Forest, ...) Similarly, a C-VAE model can be used successfully to reconstruct features in accordance with a probability distribution similar to the original features and conditioned to the detected intrusion. The results can be divided in two groups: (1) classification prediction results and (2) accuracy of synthetic features results. Considering classification results, our proposed model obtains an F1 score of 0.79 and an accuracy and recall of 0.80 which are the highest among all the algorithms studied. These metrics may not be seen as very high, but it is important to realize that these are aggregated results for a very unbalanced predicted label. When each label is considered separately, for one-vs.-rest results, we obtain a F1 score greater than 0.82 for the most frequent labels and accuracy over 0.91 for four of the five label values. The behavior of lower frequency labels is noisy due to the nature of the training and test datasets (NSL-KDD). For one-vs.-rest the accuracy obtained is always greater than 0.83 regardless of the label. Taking into account the results of synthetic (reconstructed) features, we have mainly considered the recovery of the categorical features, where the achievable accuracy is related to the number of values of the feature. For the features protocol and flag with 3 and 11 values, we have obtained an accuracy of 99% and 92% respectively, while for the feature service with 70 values the accuracy is 71% (quite good considering the large number of values to recover). The remaining metrics (F1, precision, and recall) have very similar values to the accuracy score. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 72PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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