Affiliations: [a] Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin, China | [b] Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin, China. E-mails: txl@guet.edu.cn, 773811657@qq.com | [c] Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China. E-mail: ywang@guet.edu.cn
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.
