Traffic classification algorithm of Internet of things devices based on random forest

LI Ruiguang; DUAN Pengyu; SHEN Meng; ZHU Liehuang

doi:10.13700/j.bh.1001-5965.2020.0383

Volume 48 Issue 2

Feb. 2022

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Journal of Beijing University of Aeronautics and Astronautics > 2022 > 48(2): 233-239.

Fan Yuezu, Zhang Yinan, Ma Haokai, et al. Application of the hybrid BP/GA algorithm in simple integrated navigation system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(05): 535-538. (in Chinese)

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LI Ruiguang, DUAN Pengyu, SHEN Meng, et al. Traffic classification algorithm of Internet of things devices based on random forest[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(2): 233-239. doi: 10.13700/j.bh.1001-5965.2020.0383(in Chinese)

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Traffic classification algorithm of Internet of things devices based on random forest

doi: 10.13700/j.bh.1001-5965.2020.0383

1.
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2.
National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China

More Information

Corresponding author: ZHU Liehuang, E-mail: liehuangz@bit.edu.cn
Received Date: 03 Aug 2020
Accepted Date: 05 Sep 2020
Publish Date: 20 Feb 2022

Abstract

Abstract

The traffic classification of Internet of things (IoT) devices is very important to the management of cyberspace assets. The classification technology based on statistical identification is a hot spot in current academic research. The previous algorithms were mainly based on the flow information to set up the feature vectors, but lesson the packet information. In this paper, we improve the traffic classification algorithm of IoT devices based on random forest. We set up the feature vectors with both the flow information and the flow's packet information. The experimental results show that, compared with previous algorithms, the classification accuracy of the proposed algorithm increases from 56% to 82%, the recall rate improves from 47% to 67%, the F₁ score increases from 0.43 to 0.74, and the confusion matrix correlation is also significantly improved. As a result, the proposed algorithm has a better classification effect than previous ones.
- Internet of things (IoT),
- traffic classification algorithm,
- random forest,
- feature vector,
- flow information,
- packet information

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References(15)

References

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