MACHINE LEARNING AND DEEP LEARNING MODELS FOR ELECTRONIC INFORMATION SECURITY IN MOBILE NETWORKS
Keywords:
Network, information security, cyber security, artificial intelligence, machine learning, deep learning, threats, cyber-attacks, vulnerabilities
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.
References
1. Keyt A. Smit, Dzhatinder N.D. Gupta. Neyronnye seti v biznese: metody i prilozheniya dlya
issledovatelya operatsiy [Neural Networks in Business: Methods and Applications for the Operations
Researcher], Computers & Operations Research, 2000, 27, pp. 1023-1044.
2. Zargaryan E.V., Akopdzhanyan Zh.Zh. Issledovanie avtomatizatsii kollaborativnykh robotov i
sposoby ikh primeneniya [Research of automation of collaborative robots and methods of their
application], Tekhnologii razrabotki informatsionnykh sistem TRIS-2020: Mater. X
Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii. "Tekhnologii razrabotki
informatsionnykh sistem" [Technologies for the development of information systems TRIS-
2020. Materials of the X International Scientific and Technical Conference "Technologies for
the Development of Information Systems"], 2020, pp. 218-223.
3. Zargaryan Yu.A. Zadacha upravlyaemosti v adaptivnoy avtomatnoy obuchaemoy sisteme
upravleniya [The problem of controllability in an adaptive automaton learning control system],
Tekhnologii razrabotki informatsionnykh sistem TRIS-2020: Mater. X Mezhdunarodnoy
nauchno-tekhnicheskoy konferentsii. "Tekhnologii razrabotki informatsionnykh sistem" [Technologies
for the development of information systems TRIS-2020. Materials of the X International
Scientific and Technical Conference. "Technologies for the Development of Information
Systems"], 2020.
4. Zargaryan E.V., Zargaryan Y.A., Dmitrieva I.A., Sakharova O.N. and Pushnina I.V. Modeling
design information systems with many criteria. Information Technologies and Engineering –
APITECH – 2020, Journal of Physics: Conference Series, 2020, Vol. 2085 (3), pp. 032057(1-
7). DOI:10.1088/1742-6596/1679/3/032057.
5. Dzhordzh Bodzhadziev, Mariya Bodzhadziev. Nechetkaya logika dlya biznesa, finansov i
upravleniya [Fuzzy Logic for Business, Finance and Management]. 2nd ed, Dostizheniya v
oblasti nechetkikh sistem - Prilozheniya i teoriya [Advances in Fuzzy Systems - Applications
and Theory], Vol. 23.
6. Zargaryan E.V., Zargaryan Y.A., Kapc I.V., Sakharova O.N., Kalyakina I.M and Dmitrieva
I.A. Method of estimating the Pareto-optimal solutions based on the usefulness, International
Conference on Advances in Material Science and Technology - CAMSTech-2020. IOP Conf.
Series: Materials Science and Engineering, 2020, Vol. 919 (2), pp. 022027 (1-8). DOI:
10.1088/1757-899X/919/2/022027.
7. Alamir Kh.S., Zargaryan E.V., Zargaryan Yu.A. Model' prognozirovaniya transportnogo potoka
na osnove neyronnykh setey dlya predskazaniya trafika na dorogakh [A traffic flow prediction
model based on neural networks for predicting traffic on the roads], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2021, No. 6 (223), pp. 124-132.
8. Soomro Z.A., Shah M.H., & Ahmed J. Information security management needs more holistic
approach: A literature review, International Journal of Information Management, 2016,
Vol. 36 (2), pp. 215-225.
9. Beloglazov D., Shapovalov I., Soloviev V., Zargaryan E. The hybrid method of path planning
in non-determined environments based on potential fields, ARPN Journal of Engineering and
Applied Sciences, 2017, Vol. 12, No. 23, pp. 6762-6772.
10. Zargarjan E.V., Zargarjan Ju.A., Finaev V.I. Information support for the training of fuzzy
production account balance in the conditions of incomplete data, Innovative technologies and
didactics in teaching (ITDT-2016): Collected papers, 2016, pp. 128-138.
11. Laptev A.S., Shestova E.A. Nedostatki neyro-ekspertnykh sistem upravleniya i sposoby ikh
resheniya [Disadvantages of neuroexpert control systems and ways to solve them], Nauka i
sovremennost': Mater. Vserossiyskoy nauchno-prakticheskoy konferentsii studentov i
molodykh uchenykh [Science and Modernity. Materials of the All-Russian scientific and practical
conference of students and young scientists]. Taganrog, 2021, pp. 104-106.
12. Finaev V.I., Zargaryan Yu.A., Zargaryan E.V., Solov'ev V.V. Formalizatsiya grupp podvizhnykh
ob"ektov v usloviyakh neopredelennosti dlya vybora upravlyayushchikh resheniy [Formalization
of groups of mobile objects in conditions of uncertainty for the choice of control solutions],
Informatizatsiya i svyaz' [Informatization and communication], 2016, No. 3, pp. 56-62.
13. Slimani I., Farissi I. El, et Achchab S. Artificial Neural Networks for Demand Forecasting:
Application Using Moroccan Supermarket Data, 2015.
14. Singh A.N., Gupta M.P., & Ojha A. Identifying factors of “organizational information security
management”, Journal of Enterprise Information Management, 2014.
15. Pushnina I.V. Sistema upravleniya podvizhnym ob"ektom v usloviyakh neopredelennosti
[Control system of a moving object under conditions of uncertainty. In the collection], Nauka i
obrazovanie na rubezhe tysyacheletiy. sbornik nauchno-issledovatel'skikh rabot [Science and
education at the turn of the millennium. collection of research papers]. Kislovodsk:
Kislovodskiy gumanitarno-tekhnicheskiy institut, YuFU, 2018, pp. 65-74.
16. Wang X., Wang C. Time series data cleaning: A survey, IEEE Access, 2020, Vol. 8, pp. 1866-
1881. DOI: 10.1109/ACCESS.2019.2962152.
17. Data-driven smart cities: Big Data, analytics, and security, 2018. Available at:
https://skelia.com/ articles/data-driven-smart-cities-big-data-analytics-and-security/.
18. Kim J., Tae D., Seok J. A survey of missing data imputation using generative adversarial networks,
Proc. of the 2020 Int. Conf. on Artificial Intelligence in Information and Communication,
ICAIIC 2020, pp. 454-456. DOI: 10.1109/ICAIIC48513.2020.9065044.
19. Dmitrieva I.A., Mileshko L.P., Begun O.V., Berezhnaya A.V. Information Modernization of the
General Theory of Environmental Safety Ensuring, IOP Conference Series: Materials Science
and Engineering. III International Scientific Conference. Krasnoyarsk, 2021, pp. 12072.
20. Ma Q., Johnston A.C., & Pearson J.M. Information security management objectives and practices:
a parsimonious framework, Information Management & Computer Security, 2008.
21. Shestova E.A., SHadrina V.V. Issledovanie postroeniya vidov operatsiy nad nechetkimi
mnozhestvami i nechetkoy approksimiruyushchey sistemy v srede MATLAB: ucheb. posobie
[Investigation of the construction of types of operations on fuzzy sets and a fuzzy approximating
system in MATLAB: textbook]. Taganrog, 2019.
22. Babenko L.K., Shumilin A.S., Alekseev D.M. Algoritm obespecheniya bezopasnosti
konfidentsial'nykh dannykh meditsinskoy informatsionnoy sistemy khraneniya i obrabotki
rezul'tatov obsledovaniy [Algorithm for ensuring the security of confidential data of the medical
information system for storing and processing survey results], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2020, No. 5 (215), pp. 6-16.
issledovatelya operatsiy [Neural Networks in Business: Methods and Applications for the Operations
Researcher], Computers & Operations Research, 2000, 27, pp. 1023-1044.
2. Zargaryan E.V., Akopdzhanyan Zh.Zh. Issledovanie avtomatizatsii kollaborativnykh robotov i
sposoby ikh primeneniya [Research of automation of collaborative robots and methods of their
application], Tekhnologii razrabotki informatsionnykh sistem TRIS-2020: Mater. X
Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii. "Tekhnologii razrabotki
informatsionnykh sistem" [Technologies for the development of information systems TRIS-
2020. Materials of the X International Scientific and Technical Conference "Technologies for
the Development of Information Systems"], 2020, pp. 218-223.
3. Zargaryan Yu.A. Zadacha upravlyaemosti v adaptivnoy avtomatnoy obuchaemoy sisteme
upravleniya [The problem of controllability in an adaptive automaton learning control system],
Tekhnologii razrabotki informatsionnykh sistem TRIS-2020: Mater. X Mezhdunarodnoy
nauchno-tekhnicheskoy konferentsii. "Tekhnologii razrabotki informatsionnykh sistem" [Technologies
for the development of information systems TRIS-2020. Materials of the X International
Scientific and Technical Conference. "Technologies for the Development of Information
Systems"], 2020.
4. Zargaryan E.V., Zargaryan Y.A., Dmitrieva I.A., Sakharova O.N. and Pushnina I.V. Modeling
design information systems with many criteria. Information Technologies and Engineering –
APITECH – 2020, Journal of Physics: Conference Series, 2020, Vol. 2085 (3), pp. 032057(1-
7). DOI:10.1088/1742-6596/1679/3/032057.
5. Dzhordzh Bodzhadziev, Mariya Bodzhadziev. Nechetkaya logika dlya biznesa, finansov i
upravleniya [Fuzzy Logic for Business, Finance and Management]. 2nd ed, Dostizheniya v
oblasti nechetkikh sistem - Prilozheniya i teoriya [Advances in Fuzzy Systems - Applications
and Theory], Vol. 23.
6. Zargaryan E.V., Zargaryan Y.A., Kapc I.V., Sakharova O.N., Kalyakina I.M and Dmitrieva
I.A. Method of estimating the Pareto-optimal solutions based on the usefulness, International
Conference on Advances in Material Science and Technology - CAMSTech-2020. IOP Conf.
Series: Materials Science and Engineering, 2020, Vol. 919 (2), pp. 022027 (1-8). DOI:
10.1088/1757-899X/919/2/022027.
7. Alamir Kh.S., Zargaryan E.V., Zargaryan Yu.A. Model' prognozirovaniya transportnogo potoka
na osnove neyronnykh setey dlya predskazaniya trafika na dorogakh [A traffic flow prediction
model based on neural networks for predicting traffic on the roads], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2021, No. 6 (223), pp. 124-132.
8. Soomro Z.A., Shah M.H., & Ahmed J. Information security management needs more holistic
approach: A literature review, International Journal of Information Management, 2016,
Vol. 36 (2), pp. 215-225.
9. Beloglazov D., Shapovalov I., Soloviev V., Zargaryan E. The hybrid method of path planning
in non-determined environments based on potential fields, ARPN Journal of Engineering and
Applied Sciences, 2017, Vol. 12, No. 23, pp. 6762-6772.
10. Zargarjan E.V., Zargarjan Ju.A., Finaev V.I. Information support for the training of fuzzy
production account balance in the conditions of incomplete data, Innovative technologies and
didactics in teaching (ITDT-2016): Collected papers, 2016, pp. 128-138.
11. Laptev A.S., Shestova E.A. Nedostatki neyro-ekspertnykh sistem upravleniya i sposoby ikh
resheniya [Disadvantages of neuroexpert control systems and ways to solve them], Nauka i
sovremennost': Mater. Vserossiyskoy nauchno-prakticheskoy konferentsii studentov i
molodykh uchenykh [Science and Modernity. Materials of the All-Russian scientific and practical
conference of students and young scientists]. Taganrog, 2021, pp. 104-106.
12. Finaev V.I., Zargaryan Yu.A., Zargaryan E.V., Solov'ev V.V. Formalizatsiya grupp podvizhnykh
ob"ektov v usloviyakh neopredelennosti dlya vybora upravlyayushchikh resheniy [Formalization
of groups of mobile objects in conditions of uncertainty for the choice of control solutions],
Informatizatsiya i svyaz' [Informatization and communication], 2016, No. 3, pp. 56-62.
13. Slimani I., Farissi I. El, et Achchab S. Artificial Neural Networks for Demand Forecasting:
Application Using Moroccan Supermarket Data, 2015.
14. Singh A.N., Gupta M.P., & Ojha A. Identifying factors of “organizational information security
management”, Journal of Enterprise Information Management, 2014.
15. Pushnina I.V. Sistema upravleniya podvizhnym ob"ektom v usloviyakh neopredelennosti
[Control system of a moving object under conditions of uncertainty. In the collection], Nauka i
obrazovanie na rubezhe tysyacheletiy. sbornik nauchno-issledovatel'skikh rabot [Science and
education at the turn of the millennium. collection of research papers]. Kislovodsk:
Kislovodskiy gumanitarno-tekhnicheskiy institut, YuFU, 2018, pp. 65-74.
16. Wang X., Wang C. Time series data cleaning: A survey, IEEE Access, 2020, Vol. 8, pp. 1866-
1881. DOI: 10.1109/ACCESS.2019.2962152.
17. Data-driven smart cities: Big Data, analytics, and security, 2018. Available at:
https://skelia.com/ articles/data-driven-smart-cities-big-data-analytics-and-security/.
18. Kim J., Tae D., Seok J. A survey of missing data imputation using generative adversarial networks,
Proc. of the 2020 Int. Conf. on Artificial Intelligence in Information and Communication,
ICAIIC 2020, pp. 454-456. DOI: 10.1109/ICAIIC48513.2020.9065044.
19. Dmitrieva I.A., Mileshko L.P., Begun O.V., Berezhnaya A.V. Information Modernization of the
General Theory of Environmental Safety Ensuring, IOP Conference Series: Materials Science
and Engineering. III International Scientific Conference. Krasnoyarsk, 2021, pp. 12072.
20. Ma Q., Johnston A.C., & Pearson J.M. Information security management objectives and practices:
a parsimonious framework, Information Management & Computer Security, 2008.
21. Shestova E.A., SHadrina V.V. Issledovanie postroeniya vidov operatsiy nad nechetkimi
mnozhestvami i nechetkoy approksimiruyushchey sistemy v srede MATLAB: ucheb. posobie
[Investigation of the construction of types of operations on fuzzy sets and a fuzzy approximating
system in MATLAB: textbook]. Taganrog, 2019.
22. Babenko L.K., Shumilin A.S., Alekseev D.M. Algoritm obespecheniya bezopasnosti
konfidentsial'nykh dannykh meditsinskoy informatsionnoy sistemy khraneniya i obrabotki
rezul'tatov obsledovaniy [Algorithm for ensuring the security of confidential data of the medical
information system for storing and processing survey results], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2020, No. 5 (215), pp. 6-16.
Published
2022-08-09
Issue
Section
SECTION III. SIMULATION OF PROCESSES AND SYSTEMS
