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Decision Tree Model-Based Security Situational Awareness Approach for Wireless Communication Networks.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yuan, Shuhong; Shan, Kangkang; Feng, Jieying 3 of 3

Abstract

Network security situational awareness is gaining increasing attention due to its capability to globally and dynamically detect potential network security risks. However, traditional security situational awareness models often exhibit poor classification performance, resulting in lower-than-expected acceleration and scalability ratios. In this paper, we propose a novel security situational awareness approach for wireless communication networks based on a decision tree model. First, reconfigure the category division module to categorize the attack data into four different types. Then, using time windows to segment the data flow between the network and the host promotes the design of effective security event detection mechanisms in the model. Finally, a comprehensive network security situational awareness model was constructed at the joint level using decision tree algorithm. The experimental results show that the proposed method can significantly improve the acceleration ratio, and the space occupancy ratio can reach 80, indicating that the proposed method can have a high level of processing capability and accurate perception in network security situations, providing a guarantee for the security of wireless communication networks. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/03, Vol. 34, Issue 1, p1
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156425402426
  • Accession Number:184145730
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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