Novel applications of Machine Learning to Network Traffic Analysis

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Table 3. Contingency table for predictions when employing different training and test data sets. Finally, to prove that synthetic and original data are similar, but not identical, we subtract (element-wise) the original and synthetic datasets, calling the resulting dataset as difference- dataset. If the similarity between the datasets is true, the values of the difference-dataset should have zero mean (no reproduction bias) and a relatively small standard deviation (not too small to make both datasets indistinguishable or too large to make them completely unrelated). Table 4 shows the mean and standard deviation for 20 continuous and discrete features of the above- mentioned difference-dataset. We can observe, in both cases, that the behavior is the expected one. The synthetic features exhibit variability (no exact copy) and are centered on expected values (no bias). Similarly, Figure 8 gives the distribution of values for several continuous and discrete features (upper and lower diagrams, respectively) of the difference-dataset. The Y-axis of the histograms corresponds to the number of repetitions of a given value, and the X-axis represents the values of the difference-dataset. These values are, in all cases, in the interval [- 1,1], due to the [0,1] scaling and one-hot encoding of the continuous and discrete features, respectively (Section 3.1). Table 4. Mean and standard deviation of difference between values (original vs. synthetic) for several features. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 175

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