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review-article

The rise of traffic classification in IoT networks: : A survey

Authors:

Nor Badrul AnuarAuthors Info & Claims

Volume 154, Issue C

https://doi.org/10.1016/j.jnca.2020.102538

Published: 15 March 2020 Publication History

Abstract

With the proliferation of the Internet of Things (IoT), the integration and communication of various objects have become a prevalent practice. The huge growth of IoT devices and different characteristics in the IoT traffic patterns have brought attention to traffic classification methods to address various raised issues in IoT applications. While network traffic classification has been well discussed in a number of surveys and review papers, it is still immature in IoT due to the differences in traffic characteristics in IoT and Non-IoT devices. This survey looks at the emerging trends of network traffic classification in IoT and the utilization of traffic classification in its applications. It also compares the legacy of traffic classification methods and presents an overview of traditional models. This paper extends the discussion with a taxonomy of the current network traffic classification within the IoT context. We then expose commercial and real-world use cases of the IoT traffic classification and finally outline open research issues and challenges in this domain.

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Index Terms

The rise of traffic classification in IoT networks: A survey
1. Networks
  1. Network services
    1. Network monitoring
  2. Network types

Index terms have been assigned to the content through auto-classification.

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cover image Journal of Network and Computer Applications

Journal of Network and Computer Applications Volume 154, Issue C

Mar 2020

106 pages

ISSN:1084-8045

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Elsevier Ltd.

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Academic Press Ltd.

United Kingdom

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Published: 15 March 2020

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Xia JWu MLi P(2025)SFMLFuture Generation Computer Systems10.1016/j.future.2024.107487162:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.future.2024.107487
Gu DZhang JTang ZLi QZhu MYan HLi H(2025)IoT device identification based on network trafficWireless Networks10.1007/s11276-024-03832-z31:2(1645-1661)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s11276-024-03832-z
Swarnkar MKumar R(2024)BitIoT: A Bit Level Deep Packet Inspection Method for Identification of MQTT-Based IoT Devices in the WildIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337388721:3(2866-2875)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3373887
Gilliard ELiu JAliyu A(2024)Knowledge graph reasoning for cyber attack detectionIET Communications10.1049/cmu2.1273618:4(297-308)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1049/cmu2.12736
Min ZGokhale SShekhar SMahmoudi CKang ZBarve YGokhale A(2024)Enhancing 5G network slicing for IoT traffic with a novel clustering frameworkPervasive and Mobile Computing10.1016/j.pmcj.2024.101974104:COnline publication date: 1-Nov-2024
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