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Fig. 18. Schema of datasets and their management to obtain the reported results. As an anticipation of the more detailed description of the main points of interest for the different papers, which is provided in the following sections, we can see in Fig. 19 and 20 an outline of the different datasets and models that are used in the papers. In Fig. 19 are presented the datasets used. We can see that two of them correspond to actual operational data obtained from real Operators or Service Providers, and the third is a well- known intrusion detection dataset (NSL-KDD) that has been used for many similar research studies. Due to the difficulties associated with obtaining real data for video QoE estimation, we had to obtain our own dataset of video QoE scores from real individuals. The main reasons to choose NSL-KDD as the dataset for intrusion detection have been: (a) its availability, (b) the large number of research works carried out with it that facilitate the comparison of the results with the methods proposed in this thesis, and (c) its relatively reduced size that avoids the need for large computational resources. In particular, we would like to mention the UGR16 [136] dataset, which is a modern and very relevant dataset for intrusion detection that was not included in the research due to its large size. The use of real data to conduct studies is an important advantage. It makes the results more realistic too. In addition, the “No free lunch theorem” [137] states that, on average, all algorithms are similar when faced with all possible problems, therefore, the difference between algorithms is their ability to provide a performance advantage for a particular kind of problem. With this in mind, having a realistic dataset that resembles a real environment is important in order to adapt the best algorithm to the desired real problem. For intrusion detection, the availability of realistic data is problematic due to the stochastic nature of the intrusions, their low frequency of appearance, the large number of possible types of intrusions and their dependence on the nature of the network attacked. For these reasons we have used the NSL-KDD dataset as a representative dataset for intrusion detection. The NSL- KDD [67] dataset is a derivation of the original KDD 99 dataset. It solves the problem of Doctoral Thesis: Novel applications of Machine Learning to NTAP - 63PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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