Bibliographic Details
| Title: |
Research on Network APT Attack Intrusion Detection Technology Based on Machine Learning Algorithm |
| Authors: |
Meng, Qingchuan, Yang, Yang, Wu, Fengzhi, Chen, Xiang, Chen, Xiaoming |
| Source: |
IOP Conference Series: Materials Science and Engineering; March 2020, Vol. 799 Issue: 1 p012029-012029, 1p |
| Abstract: |
The attack frequency of network advanced persistent threat (APT) is more and more higher, which seriously endangers the network security. In order to obtain high accuracy of network APT attack intrusion detection results, aiming at the limitations of current network APT attack intrusion detection model, a network APT attack intrusion detection model based on machine learning algorithm is proposed. A "one-to-one" network APT attack intrusion detection classifier is built through a neutral and excellent support vector mechanism of machine learning algorithm, and the current standard network APT attack intrusion detection database is adopted to verify the validity of the model. The accuracy of network APT attack intrusion detection is over 95%, and the detection error is far lower than the actual application range. It can be used in the actual network security management. |
| Database: |
Supplemental Index |
