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Novel applications of Machine Learning to Network Traffic Analysis

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Novel applications of Machine Learning to Network Traffic Analysis ( novel-applications-machine-learning-network-traffic-analysis )

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Figure 1. Evaluation process As for the forecasting function to maximize, the simple and well-known accuracy function has been used: π‘Žπ‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦ = π‘‘π‘œπ‘‘π‘Žπ‘™β‘π‘›π‘’π‘šπ‘π‘’π‘Ÿβ‘π‘œπ‘“β‘π‘π‘œπ‘Ÿπ‘Ÿπ‘’π‘π‘‘β‘π‘π‘Ÿπ‘’π‘‘π‘–π‘π‘‘π‘–π‘œπ‘›π‘  (1) π‘‘π‘œπ‘‘π‘Žπ‘™β‘π‘›π‘’π‘šπ‘π‘’π‘Ÿβ‘π‘œπ‘“β‘π‘π‘Ÿπ‘’π‘‘π‘–π‘π‘‘π‘–π‘œπ‘›π‘  2.4. Data First it is important to note that, in any forecasting problem there is a tradeoff between the so called bias (also known as underfitting, in which the algorithm fails to predict the relation between the features of past data and future data) and the variance (also known as overfitting, in which the algorithm only works well with a subset of past data but does not generalize well) [3]. For the forecasting problem presented here the main issue is at the bias side, which means that it is more important to discover additional important features, than increasing the amount of data. This work is based on real (from a telecommunications operator) activity data (bytes sent/received) from 6,214 mobile devices (identified by their SIMs) for a period of almost 30 consecutive days (4/4/2015 to 1/5/2015, which includes public holidays making the forecast more challenging). For every SIM available data includes the starting connection time, duration of the connection and total transmitted data. The SIMs had different origins in terms of access point (so called Access Point Name (APN)), geographic area, customer and subscribed rate plan. The exploratory data analysis showed that the connectivity data was quite irregular, not showing any representative pattern between devices. Neither was any clear pattern considering the other features like access point, region or even customer as aggregating variables. Taking into account the proposed non-time-series prediction methods, it was necessary to perform a previous preparation of data, with two main purposes: 1) Transform connection activity of SIMs to a one-hour period aggregated activity (from the initial time connection data), and 2) Present the data in a form that could be used by both the time-series and non- Doctoral Thesis: Novel applications of Machine Learning to NTAP - 98

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