5G Terminal Signaling Outlier Detection Algorithm Based on Joint Distance and Density

doi:10.3969/j.issn.1006-2475.2025.08.016

Abstract

Abstract:
Abstract: In order to quickly and accurately detect the abnormal behavior of 5G terminal signaling， this paper proposes a 5G terminal signaling outlier detection algorithm based on joint distance and density. Initially， preprocessing is applied to the 5G terminal signaling data collected by software to reduce the dimensionality of the data samples. When the number of data samples is less than 1000 after dimensionality reduction， the KNN outlier detection algorithm based on average distance is used to detect the outlier points of the data samples； when the number of data samples is greater than or equal to 1000， the COF outlier detection algorithm based on density is used to detect the outlier points. Simulation results show that the algorithm proposed in this paper outperforms the traditional benchmark algorithm in terms of accuracy， precision， and recall， and has a faster response time.

Key words: Key words: 5G terminal signaling, outlier detection, accuracy rate, precision rate, recall rate

CLC Number:

中图分类号：TP393

WEI Xiaogang1, MEI Wenming2, HU Youjun1, TU Zhengwei1, WANG Rui3 . 5G Terminal Signaling Outlier Detection Algorithm Based on Joint Distance and Density[J]. Computer and Modernization, 2025, 0(08): 115-118.

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