Article: Mobile cellular network security vulnerability detection using machine learning Journal: International Journal of Information and Communication Technology (IJICT) 2023 Vol.22 No.3 pp.327 - 341 Abstract: Due to the low monitoring accuracy and duration of the traditional cellular mobile network security infringement monitoring system, a computerised cellular mobile network intelligent blank monitoring system is proposed. It connects the blank detection module to the scanner according to the data attributes to scan the blanks in the mobile cellular network. During the tracking of cyberspace signals, the data space of the system session is controlled. Mobile cells of cellular networks introduce machine intelligence data processing learning algorithms hidden in the data. Experimental results show that ML-based cellular mobile network vulnerability detection (VD-MCN) can effectively improve system control accuracy and cellular network security space control efficiency. However, there are still some things that are ignored to improve the development efficiency of MCN, and developers often only care about themselves. Whether the corresponding functions can be realised in the process of code reuse, or there is lack of understanding, inspection and testing of the reuse code, the integration of these, can achieve our expected results. Inderscience Publishers - linking academia, business and industry through research

Title: Mobile cellular network security vulnerability detection using machine learning

Authors: Gongping Chen; Hong Wang; Chuanqi Zhang

Addresses: Lu'an Vocational Technical College, No. 1, Zhengyang Road, Lu'an City, Anhui Province, 237158, China ' Lu'an Vocational Technical College, No. 1, Zhengyang Road, Lu'an City, Anhui Province, 237158, China ' Lu'an Vocational Technical College, No. 1, Zhengyang Road, Lu'an City, Anhui Province, 237158, China

Abstract: Due to the low monitoring accuracy and duration of the traditional cellular mobile network security infringement monitoring system, a computerised cellular mobile network intelligent blank monitoring system is proposed. It connects the blank detection module to the scanner according to the data attributes to scan the blanks in the mobile cellular network. During the tracking of cyberspace signals, the data space of the system session is controlled. Mobile cells of cellular networks introduce machine intelligence data processing learning algorithms hidden in the data. Experimental results show that ML-based cellular mobile network vulnerability detection (VD-MCN) can effectively improve system control accuracy and cellular network security space control efficiency. However, there are still some things that are ignored to improve the development efficiency of MCN, and developers often only care about themselves. Whether the corresponding functions can be realised in the process of code reuse, or there is lack of understanding, inspection and testing of the reuse code, the integration of these, can achieve our expected results.

Keywords: machine learning; ML; wireless communication; network security vulnerability; intelligent monitoring; mobile cellular network; MCN.

DOI: 10.1504/IJICT.2023.129955

International Journal of Information and Communication Technology, 2023 Vol.22 No.3, pp.327 - 341

Received: 10 Oct 2022
Accepted: 04 Jan 2023
Published online: 03 Apr 2023 *

Title: Mobile cellular network security vulnerability detection using machine learning

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