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effects. We used several network protocols (HTTP, RTP and UDP) to increase the variety of video transmissions. B. Data preparation The data generated, as described in the previous section, is further processed to extract aggregate information associated with each time-step, in 1-second intervals. The new features formed by these aggregates are organized into samples, finally forming a time-series of vectors (samples). To build the training dataset, an ad-hoc application was developed as a feature extractor. The feature extractor identifies packets belonging to a specific multimedia transmission, extracting certain IP header information from the packets, namely the size of the application layer and the inter-arrival time between consecutive packets. Later, these two features are expanded in a collection of 40 statistical attributes that includes means, standard deviations, root mean squares, maximums, minimums and percentiles. In addition, the number of packets transferred in the ingoing and outgoing directions is counted and also included as a feature. All these features are normalized to the range [0-1] with a previous log normalization for features with high values ranges. Finally, the QoE information provided by the end users in terms of the possible errors observed in each time-step is appended to the collection of attributes as labels. We have evaluated seven QoE errors: columns, blur, ghost, chrominance, blockness, color bleeding and black pixel. The resulting vector time-series are described in Fig. 2.a. To train with the least possible number of samples, we perform an additional transformation of the data in Figure 2.a to arrange it in small elementary flows used for training (see Figure 2.b). Figure 2 shows the complete process to obtain and transform the training data. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 151PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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