Title: Airborne network security protection technology based on hybrid K-means algorithm

Authors: Yunna Shao; Bangmeng Xiang

Addresses: College of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, 325000, China ' College of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, 325000, China

Abstract: In order to reduce the security risks such as illegal acquisition of airborne network data and malicious attacks. Based on k-tree structure, weighted density method is used to accelerate K-means clustering. Weighted voting rules are proposed to enhance the training of labelled data sets. Finally, binary tree structure is used to design the classification model. The results showed that the detection rates of remote to local (R2L) and user to root (U2R) were increased by 7.98% and 7.64%, respectively. The research methods achieved 91.63%, 92.29%, 90.68% and 96.34% of the network information confidentiality, integrity and availability, and virus detection ability, respectively. The increases were 36.15%, 40.81%, 44.41% and 44.38%, respectively. The research model can detect airborne network attacks more comprehensively and accurately than the existing detection methods. It can be used to protect the personal information of network users, as well as the security of network communication processes.

Keywords: K-means algorithm; airborne network security; semi supervised hierarchical classification; tri-means; Kd-tree; detection accuracy.

DOI: 10.1504/IJCSYSE.2026.151336

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.13 - 26

Received: 26 Jun 2023
Accepted: 19 Aug 2023
Published online: 26 Jan 2026 *

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