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4.3 Conditional variational autoencoders for classification It is paradoxical to see the little attention that generative models have attracted in networking [8], considering their successful application in other areas, mainly with the advent of the variational autoencoder (VAE)[23] and generative adversarial networks (GAN)[135] algorithms. This is the reason to consider the application of one of these models (VAE) for this thesis. A generative model can be used to create new synthetic data, to correct/improve available data or as a mean to build classifiers/regressors. In this section we will cover the use of a VAE as a classifier [3] and in the following section how to use it to synthesize and correct data [5]. As noted in section 3.2.1 the application of a supervised model for classification can be done in three ways applying different methods: probabilistic methods, clustering methods or deviation methods. Deviation methods define a generative model that can reconstruct normal data, and any data that is reconstructed with an error greater than a threshold is considered an anomaly. Deviation methods are those used when applying a VAE for classification. When using a VAE to build a classifier it is necessary to create as many models as there are distinct label values, each model requiring a specific training step (one vs. rest). Each training step employs, as training data, only the specific samples associated with the label learned, one at a time. At test time, the comparison of the errors obtained when re-generating the features of the data using the different models will indicate which is the correct label (the one associated with the model that produces the least error). The novel approach in this thesis has been to use a Conditional VAE (CVAE) [24][25] instead of a VAE to perform classification. This is, as far as we know, the first time that a CVAE is used for this topic. The main difference between a CVAE and a VAE is presented in Fig 12. In a CVAE we employ the label (in our case, the intrusion label) as an additional input to the decoder at training time. This additional information allows the network to learn the association of features to labels. This also allows the training of a single network to be sufficient to perform the classification, while a VAE needs as many different networks as the number of label values. At test time, we simply have to run the network forward with the features and each possible label value (one run per label value) and to compare the reconstruction errors obtained for each run (Fig 13). Therefore, a CVAE needs to create a single model with a single training step, employing all training data irrespective of their associated labels. This is why a classifier based on a CVAE is a better option in terms of computation time and solution complexity. Furthermore, it provides better classification results than other familiar classifiers (random forest, support vector machines, logistic regression, multilayer perceptron), as shown in [3]. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 49PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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