Open AccessConstruction and Application of Machine Learning Model in Network Intrusion Detection
Author(s) -
Guanglei Qi,
Zhijiang Chen,
Haiying Zhao,
Chensheng Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012001
Subject(s) - intrusion detection system , computer science , network security , anomaly based intrusion detection system , support vector machine , intrusion , data mining , network model , artificial intelligence , machine learning , computer security , geochemistry , geology
The modeling of network intrusion detection is a kind of significant network security protection technology. Nowadays, the network intrusion detection model can not accurately describe the intrusion behavior, so that incomplete network intrusion detection will occur. Therefore, based on machine learning algorithm, a network intrusion detection model is designed in the paper, which supports the fact that vector machine (SVM) fits the mapping relationship between network intrusion detection characteristics and network intrusion behavior. Meanwhile, a network intrusion detection model that reflects the relationship between the two aspects is established in the paper too. As a result, the experimental results show that the model proposed in the paper can not only accurately identify the network intrusion behavior, but has a very fast detection speed. Moreover, the model proposed in the paper has obtained much better network intrusion detection results than that of other models, which is of wide application prospect.