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Abstract

Network Traffic Classification (NTC) is an important technology for network management, traffic control, security detection and so on. With the development of the high-speed, large-scale complex networks, NTC appears some challenges in area of data storage and processing for massive network traffic. Although there are a few NTC based on cloud computing, its parallel computing model has not received enough attention. In this paper, based on the Selective Ensemble and Diversity Measures, we propose a novel Parallelized Network Traffic Classification framework (PNTC-SE-DM), which is used to parallel process the large-scale network traffic data by MapReduce architecture. In particular, in PNTC-SE-DM, we present a new method to select the classifiers for ensemble classification, which is closely related to both the prediction accuracy of the single classifier and the diversity among the multi-classifiers. The experimental results demonstrate that the new approach has the advantage of tackling large-scale network traffic data, and is favorable in terms of the evaluation metrics of speedup, sizeup and accuracy.

Keywords

Network traffic classification MapReduce selective ensemble diversity measures

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A parallelized network traffic classification framework based on selective ensemble and diversity measures

Abstract

Keywords

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