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Network Security Situation Factor Extraction Based on Random Forest of Information Gain

Authors:

Yongcheng Duan,

Le YangAuthors Info & Claims

ICBDC '19: Proceedings of the 4th International Conference on Big Data and Computing

Pages 194 - 197

https://doi.org/10.1145/3335484.3335486

Published: 10 May 2019 Publication History

Abstract

Aiming at the problem of situational element extraction, a method based on random forest of information gain for network security situation factor extraction is proposed. First, the importance of the attribute is determined by the information gain. After the threshold is set, the attribute is reduced and the redundant attribute is deleted. Secondly, the processed data is classified using the random forest classifier. Finally, in order to verify the efficiency of the algorithm, the improved method is tested by the intrusion detection data set. Compared with the traditional method, the experimental results show that the algorithm effectively improves the accuracy and achieves efficient extraction of network security situation elements.

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Cited By

Peng XYu HFu Y(2024)Research on Industrial Control Network Security Data Feature Extraction Technology Based on Composite Sparse Autoencoder2024 5th International Conference on Computer Engineering and Application (ICCEA)10.1109/ICCEA62105.2024.10603581(805-809)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCEA62105.2024.10603581
Jiang TFu XWang M(2024)BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical TransformerIEEE Access10.1109/ACCESS.2024.338640512(54191-54201)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3386405
Tao XLiu ZZhao FLan RLiu RFu LOuyang Y(2023)An SSA-LC-DAE Method for Extracting Network Security ElementsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.323398610:2(1175-1185)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TNSE.2023.3233986
Show More Cited By

Index Terms

Network Security Situation Factor Extraction Based on Random Forest of Information Gain
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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ICBDC '19: Proceedings of the 4th International Conference on Big Data and Computing

May 2019

353 pages

ISBN:9781450362788

DOI:10.1145/3335484

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University
Sun Yat-Sen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 May 2019

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ICBDC 2019

ICBDC 2019: 2019 4th International Conference on Big Data and Computing

May 10 - 12, 2019

Guangzhou, China

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Cited By

Peng XYu HFu Y(2024)Research on Industrial Control Network Security Data Feature Extraction Technology Based on Composite Sparse Autoencoder2024 5th International Conference on Computer Engineering and Application (ICCEA)10.1109/ICCEA62105.2024.10603581(805-809)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCEA62105.2024.10603581
Jiang TFu XWang M(2024)BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical TransformerIEEE Access10.1109/ACCESS.2024.338640512(54191-54201)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3386405
Tao XLiu ZZhao FLan RLiu RFu LOuyang Y(2023)An SSA-LC-DAE Method for Extracting Network Security ElementsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.323398610:2(1175-1185)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TNSE.2023.3233986
Zhang HKang CXiao Y(2021)Research on Network Security Situation Awareness Based on the LSTM-DT ModelSensors10.3390/s2114478821:14(4788)Online publication date: 13-Jul-2021
https://doi.org/10.3390/s21144788
Xuan DHu HWang BLiu B(2021)Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)10.1109/CCCI52664.2021.9583206(1-5)Online publication date: 15-Oct-2021
https://doi.org/10.1109/CCCI52664.2021.9583206
Zhang HLi JLiu XDong C(2021)Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detectionFuture Generation Computer Systems10.1016/j.future.2021.03.024122(130-143)Online publication date: Sep-2021
https://doi.org/10.1016/j.future.2021.03.024
Zhang JFeng HLiu BGe GLiu J(2021)Research Progress and Future Trend Analysis of Network Security Situational AwarenessMobile Multimedia Communications10.1007/978-3-030-89814-4_39(537-549)Online publication date: 2-Nov-2021
https://doi.org/10.1007/978-3-030-89814-4_39

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