Document Type
Article
Publication Date
2018
Publication Source
IEEE Access
Abstract
A smart home network will support various smart devices and applications, e.g., home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart home access the Internet through a home gateway (HGW). In this paper, we propose a software-defined- network (SDN)-HGW framework to better manage distributed smart home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart homes. Our proposed SDN-HGW extends the control to the access network, i.e., a smart home network, for better end-to-end network management. Specifically, the proposed SDN-HGW can achieve distributed application awareness by classifying data traffic in a smart home network. Most existing traffic classification solutions, e.g., deep packet inspection, cannot provide real-time application awareness for encrypted data traffic. To tackle those issues, we develop encrypted data classifiers (denoted as DataNets) based on three deep learning schemes, i.e., multilayer perceptron, stacked autoencoder, and convolutional neural networks, using an open data set that has over 200 000 encrypted data samples from 15 applications. A data preprocessing scheme is proposed to process raw data packets and the tested data set so that DataNet can be created. The experimental results show that the developed DataNets can be applied to enable distributed application-aware SDN-HGW in future smart home networks.
Inclusive pages
55380-55391
ISBN/ISSN
2169-3536
Document Version
Published Version
Publisher
IEEE-Inst Electrical Electronics Engineers Inc
Volume
6
Peer Reviewed
yes
Sponsoring Agency
2016 Jiangsu Provincial Government Scholarship Program ; 2017 Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-Aged Teachers and Presidents ; Top-Notch Academic Programs Project of Jiangsu Higher Education Institutions ; National Natural Science Foundation of China (NSFC) ; Natural Science Foundation of Jiangsu Province
eCommons Citation
Wang, Pan; Ye, Feng; Chen, Xuejiao; and Qian, And Yi, "Datanet: Deep Learning Based Encrypted Network Traffic Classification in SDN Home Gateway" (2018). Electrical and Computer Engineering Faculty Publications. 479.
https://ecommons.udayton.edu/ece_fac_pub/479
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/ACCESS.2018.2872430