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considered these five categories as the final labels driving our results. These labels are still useful to fine-grain characterize the intrusions, and are still quite unbalanced (an important characteristic of intrusion data) yet contain a number of samples, in each category, big enough to provide more meaningful results. We use the full training dataset of 125,973 samples and the full test dataset of 22,544 samples for any result we provide concerning the training and test NSL-KDD datasets. It is also important to mention that we do not use a previously constructed (customized) training or test datasets, nor a subset of them, what may provide better-alleged results but be less objective and also miss the point to have a common reference to compare results. 3.2. Methodology In Figure 1 we compare ID-CVAE and VAE architectures. In the VAE architecture [26], we try to learn the probability distribution of data: X, using two blocks: an encoder and a decoder block. The encoder implements a mapping from X to a set of parameters that completely define an associated set of intermediate probability distributions: π(π/πΏ). These intermediate distributions are sampled, and the generated samples constitute a set of latent variables: π, which forms the input to the next block: the decoder. The decoder block will operate in a similar way to the encoder, mapping from the latent variables to a new set of Μ parameters defining a new set of associated probability distributions:β‘π(πΏ/π), from which we Μ take samples again. These final samples will be the output of our network: β‘πΏβ‘. The final objective is to approximate as much as possible the input and output of the Μ network: πΏβ‘and β‘πΏβ‘. But, in order to attain that objective, we have to map the internal structure Μ of the data to the probability distributions: π(π/πΏ) and π(πΏ/π). Μ The probability distributions π(πΏ/π)β‘and π(π/πΏ) are conditional probability Μ distributions, as they model the probability of πβ‘ and π but depend on their specific inputs:β‘π andβ‘πΏ, respectively Doctoral Thesis: Novel applications of Machine Learning to NTAP - 132PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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