Research on Traffic Recognition Algorithms for Industrial Control Networks based on Deep Learning

DOI

10.2991/icmeit-19.2019.96How to use a DOI?

Keywords

Deep learning; Industrial control system; Traffic identification.

Abstract

With the development of industrial control network and the deep integration of industry and information technology, the rapid development of industrial control system has increased dramatically, which has brought huge economic and property losses to industrial control companies. Therefore, a traffic identification technology based on deep learning is proposed, which makes full use of the characteristics of industrial network traffic signs. Combined with experiments, this technology can classify network traffic and effectively identify abnormal traffic in industrial control system network. Compared with traditional classification methods, it not only improves the accuracy of traffic identification, but also reduces the time required for classification.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yixiang Jiang
AU  - Wenjuan Wang
AU  - Chengting Zhang
PY  - 2019/04
DA  - 2019/04
TI  - Research on Traffic Recognition Algorithms for Industrial Control Networks based on Deep Learning
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 595
EP  - 601
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.96
DO  - 10.2991/icmeit-19.2019.96
ID  - Jiang2019/04
ER  -