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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, whi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915055/ https://www.ncbi.nlm.nih.gov/pubmed/35271163 http://dx.doi.org/10.3390/s22052017 |
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author | Gupta, Chaitanya Johri, Ishita Srinivasan, Kathiravan Hu, Yuh-Chung Qaisar, Saeed Mian Huang, Kuo-Yi |
author_facet | Gupta, Chaitanya Johri, Ishita Srinivasan, Kathiravan Hu, Yuh-Chung Qaisar, Saeed Mian Huang, Kuo-Yi |
author_sort | Gupta, Chaitanya |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8915055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89150552022-03-12 A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks Gupta, Chaitanya Johri, Ishita Srinivasan, Kathiravan Hu, Yuh-Chung Qaisar, Saeed Mian Huang, Kuo-Yi Sensors (Basel) Review 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. MDPI 2022-03-04 /pmc/articles/PMC8915055/ /pubmed/35271163 http://dx.doi.org/10.3390/s22052017 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Gupta, Chaitanya Johri, Ishita Srinivasan, Kathiravan Hu, Yuh-Chung Qaisar, Saeed Mian Huang, Kuo-Yi A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks |
title | A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks |
title_full | A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks |
title_fullStr | A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks |
title_full_unstemmed | A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks |
title_short | A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks |
title_sort | systematic review on machine learning and deep learning models for electronic information security in mobile networks |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915055/ https://www.ncbi.nlm.nih.gov/pubmed/35271163 http://dx.doi.org/10.3390/s22052017 |
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