Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

An Abnormal Traffic Cleaning System

Authors
Yang Li, Yanlian Zhang
Corresponding Author
Yang Li
Available Online April 2017.
DOI
10.2991/icmmct-17.2017.200How to use a DOI?
Keywords
Abnormal Traffic, Cleaning, Detection, Traffic Re-injection
Abstract

Abnormal traffic cleaning system is proposed, which includes a cleaning platform, a detection platform and a management platform. The cleaning platform is mainly deployed through the bypass to guide the flow of the attacked object to the cleaning equipment. According to the protection strategy, the attack traffic is cleaned and normal traffic, the detection platform complete the detection of traffic for the attack, the management platform to provide cleaning equipment, testing equipment, state monitoring. The system can effectively clean the abnormal traffic and improve the security of the network.

Copyright
© 2017, 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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Volume Title
Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
10.2991/icmmct-17.2017.200
ISSN
2352-5401
DOI
10.2991/icmmct-17.2017.200How to use a DOI?
Copyright
© 2017, 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/).

Cite this article

TY  - CONF
AU  - Yang Li
AU  - Yanlian Zhang
PY  - 2017/04
DA  - 2017/04
TI  - An Abnormal Traffic Cleaning System
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 1004
EP  - 1008
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.200
DO  - 10.2991/icmmct-17.2017.200
ID  - Li2017/04
ER  -