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7.2 Paper 2: Network Traffic Classifier with Convolutional and Recurrent Neural Networks for Internet of Things Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas and Jaime Lloret Network Traffic Classifier with Convolutional and Recurrent Neural Networks for Internet of Things IEEE Access, vol. 5, pp. 18042-18050, 2017. 3.244 Q1 32 (https://scholar.google.es/citations?user=3RSZbOYAAAAJ&hl=es) Published, September-2017 https://doi.org/10.1109/ACCESS.2017.2747560 7.2.1 Objectives One of the main objectives of this paper has been to apply deep learning methods to detect the type-of-service of a network flow (NTC). To do this, we have used only the headers of the network packets without using the IP or the payload data transported by the packets, since these data may have confidentiality or encryption problems. Another objective was to apply a CNN network to the vector time-series associated with the headers of the packets, and to prove that the representation of these as images is appropriate. Finally, we wanted to demonstrate that the detection results using a deep learning model can be equivalent or better than using other classic models: C4.5, Naive Bayes, SVM, KNN, MLP, Random Forest, ... 7.2.2 Datasets The main source of data for this work has been real network packets traffic (pcap) from a national internet service provider. The dataset was formed by 266160 network flows with 20 packets each. The distribution of frequencies by type-of-service was very unbalanced, with a large number of different values (detection of 108 different types of service) 7.2.3 Models We have studied the following deep learning models: RNN only, CNN only and different combinations of CNN and RNN. Authors Title Journal Impact Factor Quartile #Citations Status Link Doctoral Thesis: Novel applications of Machine Learning to NTAP - 69PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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