Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)

RF-SVM Based Awareness Algorithm in Intelligent Network Security Situation Awareness System

Authors
Gang Chen, Yu-qian Zhao
Corresponding Author
Gang Chen
Available Online November 2017.
DOI
https://doi.org/10.2991/wartia-17.2017.45How to use a DOI?
Keywords
Intelligent System, Network Security Situation Awareness, Regression Forecast, Support Vector Machine
Abstract
With the increasing outstanding of network security situation, the intelligent network security situation awareness system has been an important weapon in cyberspace battle field, its research and development are also being a matter of great urgency. How to improve accurate rate of situation awareness has been a primary key problem which must be dealt with by intelligent network security situation awareness system. A network security situation awareness algorithm based on regression forecast support vector machine (RF-SVM) was put forward. With adopting regression idea of regression, this algorithm can forecast potential threat in future network data flow referring to historical network attack data thoroughly in process of network awareness. Experiment indicates it can improve accurate rate of situation awareness effectively and reduce forecasting error.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)
Part of series
Advances in Engineering Research
Publication Date
November 2017
ISBN
978-94-6252-409-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/wartia-17.2017.45How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Gang Chen
AU  - Yu-qian Zhao
PY  - 2017/11
DA  - 2017/11
TI  - RF-SVM Based Awareness Algorithm in Intelligent Network Security Situation Awareness System
BT  - 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)
PB  - Atlantis Press
SP  - 224
EP  - 228
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-17.2017.45
DO  - https://doi.org/10.2991/wartia-17.2017.45
ID  - Chen2017/11
ER  -