Abstract
The daily deployment of new applications, along with the exponential increase in network traffic, entails a growth in the complexity of network analysis and monitoring. Conversely, the increasing availability and decreasing cost of computational capacity have increased the popularity and usability of machine learning algorithms. In this paper, a system for classifying user activities from network traffic using both supervised and unsupervised learning is proposed. The system uses the behaviour exhibited over the network and classifies the underlying user activity, taking into consideration all of the traffic generated by the user within a given time window. Those windows are characterised with features extracted from the network and transport layer headers in the traffic flows. A three-layer model is proposed to perform the classification task. The first two layers of the model are implemented using K-Means, while the last one uses a Random Forest to obtain the activity labels. An average accuracy of 97.37% is obtained, with values of precision and recall that allow online classification of network traffic for Quality of Service (QoS) and user profiling, outperforming previous proposals.
Highlights
Network monitoring and analysis are becoming more challenging due to the growth in traffic demands
The mean accuracy of the system for K-Means and random forests (RF) combination is 96.09% (Table 6) showing that the Classification Window (CW) are assigned by the system with high accuracy
We propose a new hybrid system to classify network traffic into a simple set of user activities, based on the network behav iour exhibited by the underlying application
Summary
Network monitoring and analysis are becoming more challenging due to the growth in traffic demands. There are many new applications, causing network management challenges and resulting in an increase in the complexity of identifying the applications present on the network. This identification is useful for applying QoS policies and detecting in trusions, among other tasks. Many governments are promoting laws to prevent internet service providers (ISPs) and other companies from inspecting users’ data. Due to these factors, new analysis methods such as those based on machine learning techniques have obtained an increasing popularity among academic researchers and industrial users.
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