Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

By Manuel Lopez-Martin, Belén Carro, Antonio J. Sánchez-Esguevillas, Jaime Lloret. IEEE Access, 2017

578 citations recorded by the source corpus.

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 and 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, and 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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ddbc772bff38dc7efdd61b1a9e8a0b803706cda3

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Citations in the corpus

A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, background): “[83] demonstrated flow statistics-based network traffic classification via deep neural networks.”
Deep Learning for Encrypted Traffic Classification: An Overview (2018, methodology): “For high-dimensional inputs, CNN and LSTM are reported to be more accurate [5].”
Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges (2019, methodology): “combination of 2D convolutional and LSTM layers may be conceived [23], where the output tensor of the convolutional layer is reshaped into a matrix fed as input to an LSTM unit.”
Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics (2019, methodology): “Thework in [27] uses deep learning to classify flow types such asHTTP, SMTP, Telnet, QUIC, Office365, and YouTube by considering six features namely source/destination port number, payload volume, TCP window size, inter-arrival time and direction of traffic that are extracted from the first 20 packe”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, unknown): “[93] M.”
A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, methodology): “[146] used neural networks to conduct traffic classification for IoT networks.”