МОДЕЛИ МАШИННОГО ОБУЧЕНИЯ И ГЛУБОКОГО ОБУЧЕНИЯ ДЛЯ ЭЛЕКТРОННОЙ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ В МОБИЛЬНЫХ СЕТЯХ

Abstract

Recent advances in wireless communication technologies have led to the creation of a hugeamount of data that is transmitted everywhere. Most of this information is part of an extensive andpublicly 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 datageneration and information exchange through networks. Similarly, with the increasing diversityand 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 evidencedby 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 inthe 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 ensurethe confidentiality, integrity and reliability of the system, its equipment and protocols that controlthe network, regardless of its creation, in order to manage such a complex network as a mobileone. 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 severalML and DL technologies that can be used to create a secure environment, as well as manycybersecurity threats. It is necessary to address the need to develop new approaches to ensure ahigh level of electronic data security in mobile networks, since the possibilities for improving thesecurity of mobile networks are limitless.