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PAPER 2 Network traffic classifier with convolutional and recurrent neural networks for Internet of Things Manuel Lopez-Martin (Senior Member, IEEE), Belen Carro, Antonio Sanchez-Esguevillas (Senior Member, IEEE) and Jaime Lloret (Senior Member, IEEE) Abstract— A Network Traffic Classifier (NTC) is an important part of current network monitoring systems, being its task to infer the network service that is currently used by a communication flow (e.g. HTTP, SIP...). The detection is based on a number of features associated with the communication flow, for example, source and destination ports and bytes transmitted per packet. NTC is important because much information about a current network flow can be learned and anticipated just by knowing its network service (required latency, traffic volume, possible duration...). This is of particular interest for the management and monitoring of Internet of Things (IoT) networks, where NTC will help to segregate traffic and behavior of heterogeneous devices and services. In this paper, we present a new technique for NTC based on a combination of deep learning models that can be used for IoT traffic. We show that a Recurrent Neural Network (RNN) combined with a Convolutional Neural Network (CNN) provides best detection results. The natural domain for a CNN, which is image processing, has been extended to NTC in an easy and natural way. We show that the proposed method provides better detection results than alternative algorithms without requiring any feature engineering, which is usual when applying other models. A complete study is presented on several architectures that integrate a CNN and an RNN, including the impact of the features chosen and the length of the network flows used for training. Index Terms—Convolutional Neural Network; Deep Learning; Network traffic classification; Recurrent Neural Network I. INTRODUCTION A Network Traffic Classifier (NTC) is an important part of current network management and administration systems. An NTC infers the service/application (e.g. HTTP, SIP...) being used by a network flow. This information is important for network management and Quality of Service (QoS), as the service used has a direct relationship with QoS requirements and user contracts/expectations. It is clear that Internet of Things (IoT) traffic will pose a challenge to current network management and monitoring systems, due to the large number and heterogeneity of the connected devices. NTC is a critical component in this new scenario [1, 2], allowing to detect the service used by dissimilar devices with very different user-profiles. Network traffic identification is crucial for implementing effective management of network policy and Doctoral Thesis: Novel applications of Machine Learning to NTAP - 111PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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