MACHINE LEARNING AND DEEP LEARNING MODELS FOR ELECTRONIC INFORMATION SECURITY IN MOBILE NETWORKS

Abstract

Recent advances in wireless communication technologies have led to the creation of a huge
amount of data that is transmitted everywhere. Most of this information is part of an extensive and
publicly accessible network that connects various stationary and mobile devices around the world.
The capabilities of electronic devices are also increasing day by day, which leads to more data
generation and information exchange through networks. Similarly, with the increasing diversity
and complexity of mobile network structures, the frequency of security breaches in it has increased.
This hinders the introduction of intelligent mobile applications and services, as evidenced
by the wide variety of platforms that provide data storage, data computing and application services to end users. In such scenarios, it becomes necessary to protect data and check their use in
the network and applications, as well as check their incorrect use in order to protect private information.
According to this study, a security model based on artificial intelligence should ensure
the confidentiality, integrity and reliability of the system, its equipment and protocols that control
the network, regardless of its creation, in order to manage such a complex network as a mobile
one. The open difficulties that mobile networks still face, such as unauthorized network scanning,
fraudulent links, etc., have been thoroughly studied in this article. This article also discusses several
ML and DL technologies that can be used to create a secure environment, as well as many
cybersecurity threats. It is necessary to address the need to develop new approaches to ensure a
high level of electronic data security in mobile networks, since the possibilities for improving the
security of mobile networks are limitless.