A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Today's advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

Automated summary

The advancements in wireless communication technologies have led to a significant increase in data generation. Our information is part of a global network that connects various devices. As electronic devices become more capable, more information is being generated and shared. However, the increasing complexity of mobile network topologies has also resulted in a higher incidence of security breaches, impacting the adoption of smart mobile apps and services. Protecting data and preventing misuse is essential. Research suggests that an artificial intelligence-based security model should ensure the secrecy, integrity, and authenticity of the system, its equipment, and the network protocols. This addresses the challenges faced by mobile networks, such as unauthorized network scanning and fraud links.