A method for identifying  key features of  network security situation based on decision tree algorithm

Siyao Liu; Zonghou Wu

doi:10.1117/12.3030566

5 June 2024 A method for identifying key features of network security situation based on decision tree algorithm

Siyao Liu, Zonghou Wu

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 1316363 (2024) https://doi.org/10.1117/12.3030566
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

Network security data usually exists in a large-scale and high-dimensional form, including network traffic, log records, etc. How to efficiently process and analyze such data and extract key features is a challenging problem. A network security situation key feature identification method based on decision tree algorithm is proposed to address this issue. Build a network security situation element extraction model to extract elements such as attacks and vulnerabilities. Based on the extracted results, a decision tree model is constructed. In the feature selection stage, the most representative features are selected using information gain ratio, and node splitting is performed based on the selected features to generate a decision tree model. In order to avoid overfitting problems, the decision tree is pruned and the resulting decision tree model can be used for feature identification. The experimental results show that the false alarm rate and false alarm rate of this method are low, which is conducive to ensuring the safe operation of the network.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siyao Liu and Zonghou Wu "A method for identifying key features of network security situation based on decision tree algorithm", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 1316363 (5 June 2024); https://doi.org/10.1117/12.3030566

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