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4.4 Conditional variational autoencoders for practical data synthesis As discussed earlier, a generative model can be used to create synthetic data following the probability distribution of some real features, either continuous or discrete. We propose for this thesis to employ a new generative model based on a CVAE to synthetize new data [5] and to reconstruct missing data [3]. The new method (based on CVAE) offers operational advantages (speed, simplicity) and quality in the synthesized data (similar probability distributions and improvements in their properties) compared to the usual over-sampling algorithms (e.g. SMOTE). The main difference between using a CVAE and SMOTE (and its variants) is that a CVAE is based in a latent probability distribution learned from data, instead of being based in a predefined ‘distance’ function. A CVAE does not need to assume any ‘distance’ function, or to impose rules on the importance of proximity to majority class samples, which would be additional hyper-parameters to explore. In order to achieve the best possible generation results, we have explored different probability distributions for the latent and final layers and different loss functions used to train the network. Fig 14 presents the best architecture [5] obtained with a Gaussian distribution for the latent layer and a Bernoulli distribution for the last layer, with a loss function formed by adding the log-loss of the probability distributions for the final layer with the Kullback-Leibler divergence between a standard normal prior and the Normal probability distribution parametrized by the vector of means and variances produced by the latent layer. In Fig 14 are shown the different components of the loss function. Many other configurations were tried before arriving to this selected architecture, in particular: 1) mean square error (RMSE) instead of the log-loss for the loss function, 2) different loss functions for the continuous and discrete features, 3) to use the label as the input to the encoder, instead of the features (𝑋). Fig 14. CVAE model including the labels in the decoder with Gaussian and Bernoulli distributions. Training phase. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 52PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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