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

作者：Tao Xiaoling^*; Kong Deyan; Wang Yong

来源：Journal of High Speed Networks, 2016, 22(1): 35-42.

DOI：10.3233/JHS-160535

• Diversity measure
• Ensemble classification
• Large-scale network
• Map-reduce
• Network traffic classification
• Parallel computing models
• Prediction accuracy
• Selective ensembles

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.

• 出版日期2016