Construction and Application of Machine Learning Model in Network Intrusion Detection

DOI

10.2991/pntim-19.2019.83How to use a DOI?

Keywords

Network Intrusion Detection; Machine Learning; SVM; Network Security

Abstract

The modeling of network intrusion detection is an important network security protection technology. The current network intrusion detection model can not accurately describe the intrusion behavior, resulting in incomplete network intrusion detection. Therefore, a network intrusion detection model based on machine learning algorithm was designed. In addition, support vector machine (SVM) fits the mapping relationship between network intrusion detection characteristics and network intrusion behavior, and established a network intrusion detection model that reflected the relationship between the two aspects. Finally, the experimental results showed that the model not only can accurately identify the network intrusion behavior, but also has a very fast detection speed. It has obtained better network intrusion detection results than other models, and had a wide application prospect.

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  - Qi Guanglei
AU  - Chen Zhijiang
AU  - Zhao Haiying
AU  - Wu Chensheng
PY  - 2019/11
DA  - 2019/11
TI  - Construction and Application of Machine Learning Model in Network Intrusion Detection
BT  - Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
PB  - Atlantis Press
SP  - 407
EP  - 412
SN  - 2589-4943
UR  - https://doi.org/10.2991/pntim-19.2019.83
DO  - 10.2991/pntim-19.2019.83
ID  - Guanglei2019/11
ER  -