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gives the full set of possible features, but it is important to appreciate which features have a higher importance in the detection process. Table II shows the importance of features by analyzing the detection metrics as we remove different features. The first column in Table II gives the features that are used to train the model (grouped in feature sets) and the right columns the usual aggregate performance metrics for the detection process. The chart in Fig. 9 presents the same results in a different format, to make it easier to compare different feature sets. As expected, in general, the more features render better results. But, interestingly the packets inter-arrival time (TIMESTAMP) gives slightly worse results when added to the full features set. It seems it provides some not well-aligned information with the source and destination ports, because, as soon as we take away the source and destination port, it is clear it becomes again an important feature. It is also interesting to appreciate the importance of the feature TCP window size (WIN SIZE), being more important than TIMESTAMP when operating with a reduced set of features. Table II provides metric values with a color code to make it easier to rank the results. In this color code, darker green colors mean better results whereas darker red colors mean worse results. Table II. Classification performance metrics (aggregated) vs. features employed (model CNN+RNN-2a)(Table) Fig. 9. Classification performance metrics (aggregated) vs. features employed (model CNN+RNN-2a)(Chart) Model CNN+RNN-2a was used to obtain the results presented in Table II, but the same relationship between results and feature sets was maintained when repeating this same study Doctoral Thesis: Novel applications of Machine Learning to NTAP - 122PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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