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Figure 15. CVAE model including the labels in the decoder with Gaussian and Bernoulli distributions. Generation phase. Fig 15 shows a CVAE at test time (generation phase) [5]. Once the CVAE network is trained, we no longer require the encoder block. In order to generate new synthetic data, we apply random inputs (following a standard normal distribution) to the decoder plus the label value (one-hot encoded) as an additional input. This additional input is concatenated to the nodes of an intermediate layer of the decoder block. The decoder output will be our new synthetic data. This synthetic data will follow the probability distribution of the features of the real data associated to the label value provided at generation time. In [5] other additional challenges were found, in addition to the adequate generation of synthetic data, since evaluating that synthetic multivariate data follows the same probability distribution of real data turned out to be a difficult task for which we had to develop several approaches: (1) extended histograms of the original and synthesized features; and (2) the analysis of classification results obtained from the application of original and synthesized data to several classification algorithms. The second point is especially important, because we assumed the hypothesis that two datasets are similar if they deliver similar prediction metrics when several classifiers use their data indistinctly, therefore, we try to show that we have similar classification accuracies when we do predictions with the original or the synthetic datasets without distinction, by using the predictions obtained with several ML classifiers: Random Forest, Logistic Regression, SVM and Multilayer Perceptron (MLP). The paradigm followed to reconstruct missing data [3] has been different. In this case we have followed a similar approach to the one shown in Fig 13 and it is presented in detail in [3]. In this case, we have used a discriminative approach, and, for each missing feature, we selected the value that produces the smallest reconstruction error considering all the features. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 53PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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