Volume 12 - Issue 65
/ May 2023
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http:// www.amazoniainvestiga.info ISSN 2322- 6307
DOI: https://doi.org/10.34069/AI/2023.65.05.15
How to Cite:
Bekmagambetova, G., Polukhin, A., Volodymyr, E., Denys, K., & Oksana, D. (2023). Algorithmic means of ensuring network
security and websites: trends, models, future cases. Amazonia Investiga, 12(65), 149-163. https://doi.org/10.34069/AI/2023.65.05.15
Algorithmic means of ensuring network security and websites: trends,
models, future cases
Алгоритмічні засоби забезпечення мережевої безпеки та веб-сайтів: тренди, моделі,
кейси майбутнього
Received: April 23, 2023 Accepted: June 1, 2023
Written by:
Gulmira Bekmagambetova1
https://orcid.org/0000-0002-8999-793X
Anton Polukhin2
https://orcid.org/0000-0002-3248-210X
Volodymyr Evdokimov3
https://orcid.org/0000-0001-9497-4030
Denys Kasmin4
https://orcid.org/0000-0002-3687-4688
Oksana Dmytriienko5
https://orcid.org/0000-0002-8414-1964
Abstract
The purpose of the study is to establish probable
trends in the development of algorithmic means
of network security and the protection of web
resources in the future. The research methods
used in this publication are a bibliometric
analysis of 500 relevant publications, which
allowed us to establish probable trends in the
future development of the subject field. The
study found that currently the most likely
algorithmic means of network security and
website protection that will be intensively
developed in the future are blockchain
technologies (to protect inter-resource contact),
deep and machine learning (to analyze and detect
attacks and digital anomalies), artificial
intelligence and neural networks (to develop
complex security algorithms), and predictive
analysis (to prevent possible attacks and
malicious data injections). At the same time,
technological development makes it possible to
identify alternative security tools, including
quantum and post-quantum cryptography (which
is possible due to the development of quantum
1
PhD, Associate Professor of Department of Information Technology, Kazakh University of Technology and Business, Republic of
Kazakhstan.
2
Postgraduate Student, Laboratory of Energy Markets Mathematical Modelling, G.E. Pukhov Institute for Modelling in Energy
Engineering National Academy of Sciences of Ukraine, Ukraine.
3
Candidate of Sciences in State Administration, Leading Researcher, G.E. Pukhov Institute for Modelling in Energy Engineering
National Academy of Sciences of Ukraine, Ukraine.
4
PhD in Economics, Associate Professor of Department of Social Economics, Faculty of Economy and Law, Simon Kuznets Kharkiv
National University of Economics, Ukraine.
5
PhD in Pedagogy, Associate Professor, Docent of the Department of Mathematical Analysis and Informatics, The Faculty of
Computer Science, Mathematics, Physics and Economics, Poltava V.G. Korolenko National Pedagogical University, Ukraine.
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computing), augmented reality (which is the next
iteration of the development of the interface
between machine-human interaction), biometric
identification (which is the next iteration of
authentication and recognition systems) and
DevSecOps (which is a promising technology for
the production of digital tools and systems that
have a relatively lower level of vulnerability to
known digital threats). The correlative impact of
Industry 4.0 technologies and solutions on the
studied aspects of the security sector of the
World Wide Web has been established. The
growth of the network of devices requires the
improvement of security algorithms in the
paradigm of Industry 4.0 technologies, which
will allow more effective detection and
prevention of cyberattacks and protection of user
data.
Keywords: artificial intelligence, neural
networks, machine learning, quantum
cryptography, Industry 4.0.
Introduction
The information component is becoming
increasingly important in civilizational
development, forming a virtually digital twin of
the real world, and the direct consequences of the
financial and material ties between digital and
physical reality are increasingly blurring the line
between them. Given the progressive
intensification of digitalization in virtually all
areas of human activity, the issue of ensuring
digital, network, and cyber-physical security is a
constantly relevant and urgent task, the solution
of which is largely in the realm of scientific
research (Sharma et al., 2023; Hasan et al., 2023;
Yang et al., 2023).
Statistical studies of specialized organizations
prove the importance of researching and
developing protective algorithms, tools, and
systems, as cyber-digital threats are intensifying
with the development of the digital sphere: in
particular, in 2022, more than 25 thousand digital
threats and vulnerabilities were detected, and
identified, which is 20% more than in the
previous year; in 2022, the average cost of data
loss in the world was $ 4.35 million. The largest
losses among digitalized industries in 2022 were
in the healthcare sector, with the average cost of
data loss in the world amounting to USD 10.1
million. Among the vulnerabilities that caused
the largest financial losses in 2022 are phishing
($4.91 million with a 16% increase in data
compared to 2021), losses in business
correspondence ($4.89 million with a 6%
increase in data compared to 2021), third-party
software vulnerabilities ($4.55 million with a
16% increase in data compared to 2021), and a
range of other vulnerabilities, with technical
problems and system errors being the last. The
latter fact proves that the architecture of global
cyber-digital security requires systemic, cross-
platform, and unitary solutions when organizing
the interaction of technical means that form the
Internet (Statista, 2023; National Institute of
Standards and Technology, 2023; Vulnera, 2023;
IBM, 2023).
Analytical studies on the vulnerabilities of digital
systems and facilities point to interesting
statistics: it has been found that systems that do
not have a network connection (locally isolated
systems) are more vulnerable to digital attacks,
as their local digital security perimeter has a
limited resource and information base, which
contributes to the success of cyber threats and
cyber-attacks. According to the study, the
average time to fix critical vulnerabilities is 65
days; 33% of the vulnerabilities identified on the
full stack in 2022 were found to have serious or
critical vulnerability levels; the most common
vulnerabilities at the application and API
(Application Programming Interface) level are
still related to malicious content injection
(Injection); 13.5% of enterprise vulnerabilities
are classified as high or critical vulnerability
levels; 12% of all risks accepted by isolated
systems in 2022 were critical. These analytical
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conclusions prove the failure of the evolution of
locally isolated digital and cyber-physical
systems and focus on the development of global
network security tools and systems as the only
correct strategy for sustainable civilization
development (Edgescan, 2023; Comparitech
Limited, 2023; WPScan, 2023).
Thus, we note that the technical means of
protection and damage to digital and cyber-
physical systems are currently in relative parity
(because technical vulnerabilities are not the root
cause of significant financial losses), while
global structures of the Internet require systemic
solutions to ensure the effective functioning of
the global digital security architecture (as
evidenced by the increase in financial losses from
systemic information and digital attacks), which,
given the identified trend towards deeper
integration of digital systems and means into
physical reality (according to current scientific
observations), requires an increase in the
presence of scientific research in this security
sector of sustainable civilizational development.
The purpose of the article is to study the issues of
algorithmic means of ensuring network security
and websites and to assess the prospects for their
future development.
Theoretical Framework
According to the conclusions of Alemami, Al-
Ghonmein, Al-Moghrabi, and Mohamed (2023),
the use of algorithmic network and website
security tools is critical to protecting information
and ensuring security in cloud services. Similar
conclusions about the effectiveness of
cryptographic security algorithms (in particular
in cloud services) were reached in the
publications of Chauhan, Patel, Parikh, and Modi
(2022), Lakshmi Narayanan, and Naresh (2023),
Jabbar, and Bhaya (2023), Erondu, Asani,
Arowolo, Tyagi, and Adebayo (2023), Bhagat,
Kumar, Gupta, and Chaube (2023).
In their study, Sagu, Gill, Gulia, Singh, and Hong
(2023) conclude that the use of algorithmic
network security tools and websites is important
for ensuring the security of the Internet of Things
(hereinafter IoT). They describe the design of
metaheuristic optimization algorithms for deep
learning to secure IoT environments. The main
conclusions of the study are that the use of
metaheuristic optimization algorithms for deep
learning can ensure the security of IoT network
environments, allowing for improved efficiency
and accuracy of security systems. Similar
conclusions about the effectiveness of the
technology of deep learning security algorithms
are available in the publications of the following
authors: Jose, and Jose (2023), Seh, Yirgaw,
Ahmad, Faizan, Pathak, Zaman, and Agrawal
(2023), Diaba, and Elmusrati (2023), Gheni, and
Al-Yaseen (2023).
Chen, and Lee (2023) argues that the use of
algorithmic means of ensuring network security
and websites can be realized through the use of
blockchain technology. In the article, the authors
describe the use of blockchain-based algorithms
for the development of IoT applications. The
main conclusions are that the use of blockchain
technology can ensure the security of IoT
applications by allowing data to be stored and
transmitted in a secure manner, without the risk
of unauthorized access or modification.
Khobragade, and Turuk (2023), Priyanka,
Skandan, Shakthi Saravanan, Chandramohanan,
Darshan, and Raswanth (2023), Zubaydi, Varga,
and Molnár (2023) reached similar conclusions
about the effectiveness of blockchain-based
security algorithms.
Monika, Singh, and Wason (2023) explore the
possibility of improving network security and
website protection through the analysis and
improvement of data protection algorithms. In
particular, the article describes a study of the use
of data protection algorithms in networks with
multiprotocol label switching (GMPLS)
technology. The conclusion of the paper is that
improving data encryption and authentication
algorithms can improve data security and privacy
in GMPLS networks.
The article (Zoppi et al., 2023) discusses the
possibility of improving network security and
website protection through the use of intrusion
detection algorithms. The article compares
different types of intrusion detection algorithms,
including supervised learning, unsupervised
learning, and meta-learning. The general
conclusion is that meta-learning intrusion
detection algorithms are the most effective in
detecting unknown attacks on networks and
provide high accuracy and response speed. In
addition, the article points out the importance of
researching and developing new intrusion
detection algorithms that will provide reliable
and effective protection of networks and
websites from various types of attacks. Similar
conclusions about the effectiveness of machine
learning security algorithms have been reached
in some other publications (Upreti et al., 2023;
Mughaid et al., 2023; Akhtar, & Feng, 2023;
Al-Juboori et al., 2023).
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Birrane, Heiner, and McKeever (2023) present
the results of a study on improving network
security and websites by using the security
context of Delay-Tolerant Networks. The
researchers conclude that the use of security
context allows for a more accurate and efficient
assessment of risks and threats to network
security, which helps to increase security and
privacy, as well as reduce the possibility of
attacks and network security incidents. The use
of security context is an important element in the
development of new technologies and methods to
ensure effective and reliable protection of web
resources and networks, making it essential in
improving network security.
Pradhan, Sahu, Rajeswari, Tun, and Wah (2023)
highlight the opportunities for improving
network security and websites by integrating
artificial intelligence and machine learning into
5G technology. According to the study, the
author notes that such integration can help
increase data security and privacy, improve data
transfer speeds, increase network reliability and
efficiency, and allow solving complex network
security and smart connectivity challenges.
Pawełoszek, Kumar, and Solanki (2022),
Bhuvaneshwari (2023), and Montasari (2023)
reached similar conclusions about the
effectiveness of AI-based security algorithm
technology.
The transition of many markets to electronic
trading platforms raises not only the issue of their
security but also the violation of it can have
severe consequences for the economy of a
country or even a region. For example, in their
work, Evdokimov and Polukhin (2022)
considered optimizing trading on the wholesale
electricity market, which can increase its
efficiency. However, security breaches and
illegal interference in the operations of such
electronic trading platforms bring security
concerns to the forefront, as trading failures can
lead to real disruptions in the operation of the
power system.
The use of algorithmic means of ensuring
network security and websites is a hot topic in
research and development in the modern world.
Among the most used technologies are antivirus
programs, intrusion detection and prevention
systems, access control systems, blockchain, and
others. It is also important to use artificial
intelligence and machine learning to develop
more complex and effective algorithms for
network security and websites. Blockchain,
artificial intelligence, deep learning, and machine
learning are key technologies used to provide
network and website security. Blockchain can
ensure data security and privacy by storing
information in distributed networks with blocks
that cannot be altered or deleted without the prior
consent of all network participants. Artificial
intelligence and machine learning can help detect
and prevent malicious attacks on networks and
websites, as well as develop effective security
algorithms and identify vulnerabilities. Deep
learning is used to recognize and classify
patterns, which helps to identify malicious
objects and analyze the risks of using a malicious
program. The use of these technologies can help
ensure a high level of security for networks and
websites, reduce the risks of their vulnerability to
malicious attacks, and increase the efficiency of
networks and websites. Research and
development in this area is aimed at improving
the security of networks and reducing the risks of
their vulnerability to malicious attacks.
Methodology
In connection with the identified layers of non-
systematic information in previous studies on the
use of various algorithmic means of ensuring
network security and websites in the context of
generalizing and highlighting trends, models, and
cases of future development of the global
architecture of digital and cyber-physical
security, it is advisable to apply the methods of
bibliometric analysis of the focal area of the
scientometric landscape in the current study.
Bibliometric analysis requires the use of
specialized software that allows formulating an
analytical information array in two iterations: (1)
collection of relevant scientometric information
in a selected current search horizon; (2)
taxonomic analysis of the collected information
with the subsequent formation of relevant
analytical conclusions (Table 1).
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Table 1.
Analysis of software and digital tools and resources for bibliometric analysis
Tool name
descriptionAnalytical
Tools for collecting scientometric information
CRExplorer
Citation research is an important tool in analyzing scientific research, so Cited References
Explorer uses data downloaded from Scopus and Web of Science databases to perform
analysis over time. This tool is typically used to identify influential publications in citation
a particular scientific field, which makes it indispensable in academic impact research
thor.github.io/CRExplorer).-(https://andreas
Publish or Perish
Software that allows you to retrieve information from several databases, such as Web of
Science, Scopus, Google Scholar, Microsoft Academic, and CrossRef. This tool is widely
used to assess the academic impact of research and authors, which has made it
ispensable for the scientific community. In particular, the program allows you to study ind index), which allows you to assess the -the number of citations and the Hirsch index (h
scientific impact of individual researchers and their publications
perish).-or-ng.com/resources/publish(https://harzi
ScientoPyUI
source software that allows users to import data downloaded from Scopus and -The open
Web of Science databases to conduct scientific citation analysis. In particular, the program
ex and other important indicators that allow you to assess the ind-allows you to find the H
scientific impact of individual researchers and their publications. This tool is open for use
and can be useful for researchers who want to perform a detailed analysis of scientific data
nd influential publications in their field and fi
(https://github.com/jpruiz84/ScientoPy/blob/master/README.md).
Tools for taxonomic analysis of scientometric data
VOSviewer
networks A software tool designed to “build and visualize bibliometric networks”. These
can, for example, include journals, researchers, or individual publications and can be built
-citation, or co-on the basis of citation relationships, bibliographic relationships, co
data and visualization in a uthorship. This tool allows for detailed analysis of bibliometrica
convenient and understandable format (https://www.vosviewer.com).
CiteSpace
based software tool used to analyze trends and patterns in the scientific literature. -A Java
The tool uses data from the Web of Science as well as other sources such as arXiv,
PubMed, and NSF Award Abstracts. CiteSpace allows users to perform various
citation analysis, outlier detection, keyword analysis, -ometric analyses, including cobibli
and visualize the results using network and time zone maps
(http://cluster.cis.drexel.edu/~cchen/citespace)
Bibliometrix
tometric analyses. It uses data from source tool used for complex scien-R is an open
Scopus, Web of Science, Dimensions, PubMed, and Cochrane. Requires deep knowledge
coders -of R, but has a new version (biblioshiny) that is designed for non
(https://www.bibliometrix.org)
Gephi
or creating and visualizing network graphs that allows users to source software f-An open
import data from almost any file format. To fully use the program, you need to know Java
and/or OpenGL. Gephi allows you to perform a variety of analytical tasks on graphs, such
nalysis, identifying central nodes, and tracking changes in the network as community a
over time (https://gephi.org)
Sci2
source visualization and analysis tool developed by scientists and librarians for -An open es, including Scopus, Web of scientists. Sci2 allows you to use data from various sourc
Science, MEDLINE, and others, to study scientific publications, including scientific
relationships, references, citations, and author groupings
(https://sci2.cns.iu.edu/user/index.php)
Source: created by the authors based on descriptions from software development sites
Taking into account the specifics of the study, we
choose Publish or Perish as the software for
collecting scientometric data, which has a
comprehensive set of scientometric tools. As a
digital software tool for taxonomic analysis of
the selected information array of scientometric
data, we will use the leading industry tool -
VOSviewer, which has significant advantages
over other software (Table 1): multi-format
output data, intuitive interface, an informative
graphical adaptation of taxonomic analysis, etc.
With the help of VOSviewer, it is expected to
determine the likely vectors of future
development of algorithmic network security
tools and websites.
Thus, the research scheme proposed for
implementation in the current publication
involves the following stages of bibliometric
analysis:
1. Formation of an information array of
scientometric data based on relevant
scientific papers and publications in the
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current search horizon using the digital
software tool Publish or Perish. To ensure
the indirectness and independence of the
research results, a scientometric horizon of
500 specialized publications in a specific
research vector on algorithmic means of
ensuring network security and websites is
taken into account for analysis.
2. Transfer of the generated information array
of scientometric data to the VOSviewer
software, which further generates taxonomic
schemes that determine the likely vectors of
development of algorithmic means of
ensuring network security and websites.
3. Formation of analytical conclusions (based
on the results of the previous stages of the
study) on the future development of
algorithmic network security technology and
websites.
The implementation of the proposed research
scheme will provide far-sighted analytical
conclusions about the security sector and will
allow potential researchers to focus on
unresolved issues and specific problems.
Results and Discussion
Results of the formation of a separate section of
the scientometric landscape on the technology of
algorithmic means of ensuring network security
and websites using Publish or Perish software -
Table 2.
Table 2.
An information array of relevant publications and scientific papers created in the Publish or Perish
software
Parameter
Meaning
Query
Algorithmic technology of network security from 2018 to 2023
Source
Web of Science, Scopus, Google Scholar, Microsoft Academic, CrossRef
Papers
500
Citations
114805
Years
5
Cites_Year
22961.00
Cites_Paper
229.61
Cites_Author
46760.30
Papers_Author
181.60
Authors_Paper
3.58
h_index
176
g_index
312
hc_index
197
hI_index
49.09
hI_norm
100
AWCR
38725.80
AW_index
196.79
AWCRpA
18984.41
e_index
217.47
hm_index
117.81
QueryDate
12.04.2023 23:52
Cites_Author_Year
9352.06
hI_annual
20.00
h_coverage
68.2
g_coverage
85.2
star_count
493
year_first
2018
year_last
2023
ECC
114805
acc1
500
acc2
500
acc5
496
acc20
461
hA
82
Source: created by the author at Publish or Perish
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According to the formed information array
(Table 2), made using data from Web of Science,
Scopus, Google Scholar, Microsoft Academic,
and CrossRef, 500 articles were collected for the
period from 2018 to 2023 by the query
“Algorithmic technology of network security”.
The total number of citations amounted to
114805 (which indicates a great interest of the
scientific community in this topic), which gives
a Cites_Year index of 22961.00 and a
Cites_Paper index of 229.61. The Cites_Author
and Papers_Author indicators are 46760.30 and
181.60, respectively. On average, there are
almost 2 papers per author, which is reflected in
the Authors_Paper index of 1.58. The Hirsch
index (h_index) is 176, and its variants g_index
and hc_index are 312 and 197, respectively. The
value of the hI_index is 49.09, and hI_norm is
100. AWCR and AW_index are 38725.80 and
196.79, respectively, and AWCRpA is 18984.41.
The efficiency index (e_index) is 217.47, and the
hm_index is 117.81. The high AWCR,
AW_index, and e_index scores demonstrate the
high scientometric activity of researchers in this
field. According to the report, the number of
citations per year per author is 9352.06, and the
hI_annual index is 20.00. The h_coverage and
g_coverage coefficients are 68.2 and 85.2,
respectively. The results of the analysis allowed
us to mark 493 stars, with the first year of
publication being 2018 and the last year being
2023 (which may be due to an increase in the
number of citations of some outstanding works).
The total number of articles that have received at
least one citation is 500, which resulted in acc1,
acc2, acc5, and acc20 scores of 500, 500, 496,
and 461, respectively. It is noted that the average
number of authors per article is 1.64, which is
reflected in the hA index of 82.
In accordance with the proposed research
scheme, the relevant information array of
scientometric data generated in the Publish or
Perish software is transferred to the digital
software environment of the VOSviewer
software.
Using the built-in tools of the VOSviewer
software, we obtained a taxonomic scheme of the
selected area of the scientometric landscape for
the query “Algorithmic technology of network
security” (Figure 1).
Figure 1. The taxonomic scheme formed on the results of 500 relevant scientific publications on the query
“Algorithmic technology of network security” for the period 2018-2023.
Source: created by the author in VOSviewer software
The resulting taxonomic scheme (Figure 1)
contains 482 taxonomic units (with binary
simplification of calculations), which are
combined into 19 clusters, interconnected by
9435 main links (with their full number - 15328
units). To perform analytical conclusions, the
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modulation method of normalization of
taxonomic units was used.
In order to determine the trends in the future
development of algorithmic means of network
security and protection of web resources, we
perform a dynamic analysis of the obtained field
of relevant taxonomy (Figure 2).
Figure 2. The taxonomic scheme formed based on 500 relevant scientific publications on the query
“Algorithmic technology of network security” for the period 2018-2023 and adapted to the chronometric
dynamics of the development of clustered research vectors (trends)
Source: created by the author in VOSviewer software
According to the timeline adapted to the
dynamics of the clustered vectors that make up
the selected relevant area of the scientometric
landscape (Figure 2), we will identify the current
research trends that are likely to have the greatest
impact on the development of the subject area in
the future (Figure 3):
1. Development of blockchain technology. In
the future, blockchain technology will
continue to evolve as an effective
algorithmic means of ensuring network
security and website protection. It is
predicted that blockchain will be used to
create secure decentralized networks where
digital assets can be stored, exchanged, and
transferred without intermediation. Also,
blockchain can be used to develop secure
systems for identifying and authenticating
users on the Internet. Another area of
development of blockchain technology may
be its use to protect against cyberattacks and
increase the reliability of network protocols.
However, in order to achieve these goals, it
is necessary to investigate and solve the
problems of scalability and efficiency of
blockchain technology (Figure 3 (a)).
2. Development of deep learning technology.
The future development of deep learning
technology opens up new opportunities to
improve the security of networks and
websites. The use of deep learning can help
detect and prevent cyberattacks, as well as
ensure the security of web applications and
networks. Future developments in this
technology may include expanding
functions, such as improving risk analysis
and identifying new threats, as well as
improving the effectiveness of attack
protection by training models on a variety of
data and applying new deep learning
techniques such as reinforcement learning
and generative adversarial networks (Figure
3 (b)).
3. Development of machine learning
technology. Machine learning is expected to
continue to evolve in the context of the
website and network security. With the help
of learning algorithms, it will be possible to
automatically detect vulnerabilities and
potential threats to the network and website,
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respond quickly to incidents, and prevent
attacks. In particular, machine learning is
expected to be used to develop more
effective algorithms for monitoring,
detecting, and predicting malicious actions,
as well as to develop new systems for
protecting against cyberattacks (Figure 3
(c)).
4. Development of artificial intelligence
technology. Future developments in
artificial intelligence technology may
include increasing the efficiency and
accuracy of detecting threats and attacks on
networks and websites. Artificial
intelligence may be used to prevent
cyberattacks and predict future threats. New
methods of interactive learning may also be
developed to engage people in the process of
identifying and combating cyber threats
(Figure 3 (d)).
5. Development of neural network technology.
The future development of neural network
technology as one of the algorithmic means
of network security and website protection
involves their increasing use in cyberattack
detection and prevention systems. To
achieve this goal, neural networks will be
developed taking into account the needs of
security and attack resistance, in particular,
risk management algorithms and methods of
increasing attack resistance will be applied.
Neural network technology is also expected
to be used to develop intelligent systems for
monitoring network activity and predicting
the risks of website hacking. One of the most
promising areas is the development of neural
networks for detecting and analyzing
abnormal activity in networks, which will
allow for quick detection and response to
cyberattacks (Figure 3 (e)).
6. Development of predictive analysis
technology. Future developments in
predictive analytics technology include the
increased use of machine learning and
artificial intelligence to predict future threats
and identify critical risks in networks and
websites. The development of Big Data and
Cloud Computing technologies will allow
the use of large amounts of data to build
more accurate models that will increase the
accuracy of predictive analysis. Such models
can be used to identify threats that were not
known before and predict the likelihood of
their occurrence in the future (Figure 3 (f)).
(a)
(b)
(c)
(d)
(e)
(f)
Figure 3. Identification of trends in the likely development of algorithmic means of network security and
website protection in the future: (a) blockchain, (b) deep learning, (c) machine learning, (d) artificial
intelligence, (e) neural networks, (f) predictive analysis.
Source: created by the author in VOSviewer software
The identified trends in the future development
of algorithmic means of network security and
website protection (Figure 3) correlate with the
conclusions about the prospects for the
specialized use of these tools in the security
sector of the World Wide Web, which contain the
relevant publications of the authors:
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Hrynchyshyn (2021), Yarmoliuk (2022), Shiau,
Wang, and Zheng (2023).
At the same time, in addition to the identified
probable and promising vectors of development
of algorithmic tools for the security sector of the
Internet, there are the latest developments,
among which the following cases should be
highlighted:
1. Quantum and post-quantum cryptography.
Quantum cryptography uses the principles
of quantum mechanics to ensure the security
of data transmission. With the future
development of quantum computers that are
capable of unraveling complex encryption
algorithms, post-quantum cryptography is
becoming increasingly important for
securing networks and websites. This
technology uses mathematical principles to
create strong cryptographic systems that
cannot be decrypted even by quantum
computers. As interest in post-quantum
cryptography grows, it could become an
important algorithmic tool for network and
website security in the future (Shalini et
al.2023; Yi, 2023; Gazdag et al., 2023).
2. Augmented reality. In recent years,
augmented reality (AR) technology has
evolved significantly, especially in the field
of network security and website security.
AR can be used to create virtual training
scenarios to help users recognize threats and
learn how to respond to them. Also, AR can
be used to visualize data from various
sources, which will help identify possible
security breaches and prevent them. The
future development of AR technology
envisages the growth of its use in such
industries as medicine, military, and other
areas where detailed and accurate data
analysis is essential. The use of AR for
website security involves the development
of new technologies, such as virtual
blockchains and smart contracts, which will
ensure more efficient and secure data
exchange on the network (Herbert et al.,
2022; Alzahrani, & Alfouzan, 2022; Harris
et al., 2023).
3. Biometric identification. Future
developments in biometric identification
technology include increasing the accuracy
and speed of identification using biometric
data such as fingerprints, facial recognition,
and others. The use of biometric data for
identification may become increasingly
common in websites and network security,
where it can be used to improve security and
user experience. Progressive developments
in artificial intelligence and machine
learning technologies may lead to even more
accurate and efficient biometric
identification systems. However, there are
potential privacy and personal data
protection issues that should be considered
and addressed in the future (Brogan et al.,
2023; Shalini, 2023; Yadav et al., 2023).
4. Development of websites and applications
with regard to possible DevSecOps
vulnerabilities. DevSecOps is a combination
of DevOps practices and security principles.
This technology includes security testing
tools, automated monitoring, and data
analysis to identify vulnerabilities. It is
expected that the future development of
DevSecOps will be aimed at even greater
integration of security into software
development, as well as the use of other
algorithmic network security tools, such as
artificial intelligence, machine learning,
blockchain, and others. An important part of
DevSecOps development will be the
integration of augmented reality to display
security monitoring data and track critical
vulnerabilities. Similar conclusions were
reached in the publications (Li, &
Zalialetdzinau, 2022; Martelleur, & Hamza,
2022; Dupont et al., 2023).
In general, in the context of the future
development of algorithmic means of network
security and protection of web resources, there is
a correlative influence of the technologies of the
fourth wave of industrial development (Industry
4. 0), which is agreed by researchers Ferencz,
Domokos, and Kovacs (2021), Saura,
Ribeiro-Soriano, and Palacios-Marqués (2022),
Fernando, Tseng, Wahyuni-Td,
de Sousa Jabbour, Chiappetta Jabbour, and
Foropon, (2023). The fourth wave of the
industrial revolution, associated with the growth
in the number of devices connected to the
network, has necessitated the improvement of
algorithmic means of ensuring network security
and protecting web resources. In particular, the
introduction of smart devices and the expansion
of the Internet of Things have led to an increase
in the risk of cyberattacks. Therefore, modern
algorithmic tools for network security and web
resource protection should be improved by using
the latest Industry 4.0 technologies, such as
artificial intelligence, machine learning, data
analytics, blockchain, Internet of Things, etc.
Such tools allow for the development of more
efficient algorithms, real-time security
monitoring, detection and prevention of
cyberattacks, and protection of users' personal
data. It will also ensure the security of built
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model support tools and various simulation
models designed to perform analysis and
calculations based on real input data that may be
confidential. In the future, the development of
Industry 4.0 technologies to the following
variable iterations may lead to the emergence of
new algorithmic means of network security and
protection of web resources that will be more
efficient and reliable.
Conclusions
This study aimed to establish the prospects for
the development of algorithmic means of
network security and website protection in the
future. Based on the results of the bibliometric
analysis of 500 relevant publications published in
the period from 2018 to 2023, the probable
directions of future development of the subject
area were established, in which the following
trends were identified:
1. Blockchain. Blockchain continues to evolve
as an algorithmic tool for network security
and website protection, used to create secure
decentralized networks, user identification,
and authentication systems, protect against
cyberattacks, and increase the reliability of
network protocols but requires research on
scalability and efficiency.
2. Deep learning. The future development of
deep learning technology may open up new
opportunities to improve the security of
networks and websites, including improved
risk analysis and detection of new threats, as
well as increased effectiveness of defense
against attacks using new deep learning
methods such as reinforcement learning and
generative adversarial networks.
3. Machine learning. Machine learning will
continue to evolve to automatically detect
vulnerabilities and potential threats to the
network and websites, as well as to develop
more effective algorithms for monitoring,
detecting, and predicting malicious actions,
and new systems for protecting against
cyberattacks.
4. Artificial intelligence. Future developments
in artificial intelligence technology can
increase the accuracy and effectiveness of
detecting threats and attacks on networks
and websites, including predicting future
threats and new interactive learning methods
that engage people in the process of
combating cyber threats.
5. Neural networks. The future development of
neural network technology involves their
increasing use in cyberattack detection and
prevention systems, taking into account the
needs of security and attack resistance, as
well as the development of intelligent
systems for monitoring network activity and
detecting and analyzing anomalous activity
in networks.
6. Predictive analysis. The future development
of predictive analytics technology involves
the use of machine learning and artificial
intelligence to predict future threats and
identify critical risks in networks and
websites using Big Data and Cloud
Computing technologies.
The identified trends in the development of
algorithmic means of network security and
protection of web resources are most likely in the
near future, but at the same time, technological
development allows us to consider alternative
technological capabilities of the security sector
of the World Wide Web, which are determined
by the following trends:
1. Quantum and post-quantum cryptography as
a result of technological development and
increase of computing power of quantum
computers. The future development of
quantum computers makes post-quantum
cryptography important as a mathematical
technology for creating reliable
cryptographic systems that ensure the
security of data transmission in networks
and websites.
2. Augmented reality as the latest interface for
machine-human interaction. With the help of
augmented reality, you can create virtual
training scenarios and visualize data to
detect possible security breaches, making
AR an important algorithmic tool for
network security and website protection in
the future.
3. Biometric identification as a secure
authentication and recognition technology.
The future development of biometric
identification involves increasing the
accuracy and speed of biometric
identification, which can improve the
security and convenience of websites and
networks but also requires attention to
privacy and personal data protection issues.
4. DevSecOps as a technology for developing
invulnerable tools and systems. The future
development of DevSecOps involves even
greater integration of security into software
development and the use of other
algorithmic network security tools,
including augmented reality to display
security monitoring data and track critical
vulnerabilities.
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The study made it possible to establish the
correlative impact of technological solutions of
the fourth wave of industrial development on the
implementation of security algorithms in
network systems and web resources. The
researchers agree that the growth of network-
connected devices and the expansion of the
Internet of Things require the improvement of
algorithmic means of network security and
protection of web resources through the use of
Industry 4.0 technologies, such as artificial
intelligence, machine learning, data analytics,
blockchain, and the Internet of Things, which
will allow the development of more efficient
algorithms, detect and prevent cyberattacks, and
ensure the protection of users' personal data. In
the future, it is advisable to investigate the
possible connection between the identified trends
in the future development of algorithmic digital
security tools in the general paradigm of the next
iteration of Industry 5.0.
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