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Erman ́s semi-supervised methods [17, 18, 19, 20, 21]. A Directed Acyclic Graph-Support Vector Machine is proposed in [22], attaining an average accuracy of 95.5%. The method is applied to a one-to-one combination of classes with a dataset provided by the University of Cambridge (Moore dataset) [11]. Yamansavascilar et al. [23] study the application of several algorithms: J48, Random Forest, Bayes Net, and kNN to UNB ISCX Network Traffic dataset, with 14 classes and 12 features, reporting the best accuracy of 93.94%. Yuan et al. [24] present a variant of decision tree algorithm C4.5 working on the Hadoop platform. They classify 12 labels giving a one vs. rest accuracy in the interval 60-90% with only two labels higher than 90%. The dataset is the Moore set from Cambridge University [11]. In this paper, we present the first application of the RNN and CNN models to an NTC problem. The combination of both models provides automatic feature representation of network flows without requiring costly feature engineering. III. WORK DESCRIPTION Following sections present the dataset used for this work and a description of the different deep learning models that were applied. A. Selected dataset For this work, we have made use of real data from RedIRIS. RedIRIS is the Spanish academic and research backbone network that provides advanced communication services to the scientific community and national universities. RedIRIS has over 500 affiliated institutions, mainly universities and public research centers. We have extracted 266,160 network flows from RedIRIS. These flows contained 108 distinct labeled services, with a highly unbalanced frequency distribution. Fig. 1 shows the names and frequency distribution for the 15 most frequent services. The frequency distribution is based on the proportion of flows with a specific service. Fig. 1. Frequency distribution of the 15 most frequent services. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 114PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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