Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE) is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN), and support vector machine (SVM)) by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.

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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.