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We have produced several performance metrics: Accuracy, Precision, Recall and F1. The F1 score has been considered the most important due to the unbalanced nature of the dataset. The impact of the different features and the number of packets per flow has been extensively analyzed. 7.2.4 Results/Conclusions It has been proved that a combination of CNN and RNN models is applicable to NTC with excellent results, as well as that a temporal series of vectors can be assimilated to an image. A model formed by a combination of CNN and RNN gives the best prediction results, although an exclusive RNN model also gives excellent results. Although we have used 20 packets per flow, good prediction results can be obtained using very few packages per flow (5-15). These results are achieved despite having a large number of label values (108) with a very unbalanced distribution. Considering aggregated results (weighted average over all labels), the best model attains an accuracy of 0.9632, an F1 score of 0.9574, a precision of 0.9543 and a recall of 0.9632. Similarly, considering One-vs.-Rest results (for each label separately) for all labels with a frequency higher than 1% we achieve accuracy always higher than 98%, and many cases higher than 99%, and an F1 score higher than 0.96. For labels with a frequency lower than 1% the results are worse with more variability. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 70PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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