Mobile cellular network security vulnerability detection using machine learning

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. Copyright © The Author(s) 2022. Published by Inderscience Publishers Ltd.