=Paper=
{{Paper
|id=Vol-2616/paper28
|storemode=property
|title=Method of Cybersecurity Level Determining for the Critical Information Infrastructure of the State
|pdfUrl=https://ceur-ws.org/Vol-2616/paper28.pdf
|volume=Vol-2616
|authors= Sergiy Gnatyuk, Viktoriia Sydorenko, Artem Polozhentsev, Andriy Fesenko, Nurbol Akatayev, Gulnaz Zhilkishbayeva
|dblpUrl=https://dblp.org/rec/conf/coapsn/GnatyukSPFAZ20
}}
==Method of Cybersecurity Level Determining for the Critical Information Infrastructure of the State==
https://ceur-ws.org/Vol-2616/paper28.pdf
Method of Cybersecurity Level Determining for the
Critical Information Infrastructure of the State
Sergiy Gnatyuk 1,2,3[0000-0003-4992-0564], Viktoriia Sydorenko 1[0000-0002-5910-0837],
Artem Polozhentsev 1[0000-0003-0139-0752], Andriy Fesenko4 [0000-0001-5154-5324],
Nurbol Akatayev 5[0000-0001-5139-0792] and Gulnaz Zhilkishbayeva 3[0000-0001-9955-5994]
1
National Aviation University, Kyiv, Ukraine
2
State Scientific and Research Institute of Cybersecurity Technologies and
Information Protection, Kyiv, Ukraine
3
Yessenov University, Aktau, Kazakhstan
4
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
5
Satbayev University, Almaty, Kazakhstan
s.gnatyuk@nau.edu.ua, v.sydorenko@ukr.net,
artem.polozhencev@gmail.com, aafesenko88@gmail.com
Abstract. Protection of the state’s critical information infrastructure is a complex
process, which requires effective tools for entities’ identification, assessing their
criticality, threat and vulnerability assessment, protection against threats and also
determining the cybersecurity level of the individual entities, industries, regions,
and countries. The conducted analysis is shown that today there is no complex,
multifunctional method which helps to evaluate the cybersecurity level of the
critical information infrastructure entity or a certain industry of the state. With that
in mind, in this paper the method of determining the cybersecurity level of the
state’s critical information infrastructure was developed, taking into account the
advantages and disadvantages of the known approaches. The method will be
useful for CSIRT groups (or any other parties, who is responsible for
cybersecurity in organization) to analyze a particular industry and evaluate its
cybersecurity level. The developed method allows to calculate quantitative
parameters, describing the analyzed sector, also to compare the security level of
the critical entity before and after implementation of certain security measures.
For example, the usage of the mentioned method in the civil aviation was shown
but it can be used in various critical infrastructure sectors.
Keywords: cybersecurity index, critical information infrastructure, civil
aviation, cybersecurity level determining.
1 Introduction
The current trends in the development of information and communication
technologies (ICT) caused a phenomenal dependence on social services, which are
provided by various sectors of infrastructure. Today, with the cutting-edge
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technologies, fundamentally new global concepts have emerged, such as information
and cyber space, cybersecurity, cyber threat, critical infrastructure (CI), which have
nearly unlimited power and a leading role in the economic and social development of
each country in the world. However, in addition to its benefits, there are a number of
problems caused by the growing vulnerability of the information assets from external
cybersecurity impact, which the world’s community has also received.
A number of planned in advance, well-executed attacks in cyberspace increase
every year, these are so-called APT attacks (Advanced Persistent Threat). According
to that, there is a need to control and further regulate the relevant relationships in
cyberspace, and therefore urgently creat a reliable cybersecurity system [1].
In October 2017, the Parliament of Ukraine has signed the Law “On Basic Principles of
Cybersecurity of Ukraine” [2], in that paper Article 8 clearly describes the definitions,
main tasks and stages of the National Cybersecurity System operation. In order to provide
the necessary protection (vital interests of the individual, society and state, national
interests of Ukraine in cyberspace) of critical information infrastructure (CII) sectors,
according to [2], it is necessary to constantly maintain and improve the National
Cybersecurity System of Ukraine, by developing and rapidly adapting the public
cybersecurity policie; creating a legal and terminological framework for cybersecurity;
establishing the mandatory information security requirements of CII sectors; involing the
expert scientific institutions, professional and public associations in the preparation of
conceptual documents in the cybersecurity field; conducting drills for emergency
situations in cyberspace; developing and improving the technical and cryptographic
information protection systems; ensuring compliance with the requirements of the
legislation on protection of state’s information resources and public information;
periodicaly review the National Cybersecurity System; developing the cybersecurity
indicators etc.
1.Identification the state CII 2.Assessment the importance 3.Assessment vulnerabilities
objects and forming the list of (criticality) of the state CII and threats of the state CII
them objects objects
4.Development and
5.Identification the level of
implementation methods and
cybersecurity of state CII
tools of the state CII objects
objects
protection
Fig. 1. The general scheme of the state CIIP stages
Rather complicated issues are the development of appropriate indicators and
determination of the required protection level of cybersecurity, according to which the
price of the security system will not be higher than the usefulness of the information
to be protected. This problem can be solved, for example, by determining the
necessary level of cybersecurity for a certain facility or relevant CII sector, according
to basic approach, presented in [3] and CIIP concept (Fig. 1, Stage 5).
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2 Related research analysis and problem statement
In 2016, the International Telecommunication Union (ITU) conducted a complex
study of the cybersecurity level of 143 countries.
In 2017 the main results were announced in the report [4], accroding to which, the
method for assessing state’s security in cyberspace was proposed. It has five pillars –
Legal Measures, Technical Measures, Organizational Measures, Capacity Building,
and Cooperation. There are twenty-five pillars in total of all indicators. Global
Cybersecurity Index (GCI) is calculated as the arithmetic mean of all pillars. A
representative from an analised state should answer the 157 question to compleate the
poll. Having received the answers, ITU has defined a state’s index and a global
ranking list was created. State’s security level in cyberspace takes value from “1”
(highest) to “0” (smallest). What allows to define a worldwide overwiev of the
cybersecurity level, to assess the protection level in some parts of the world and to
analyze the cybersecurity level of each state separetly. The main disadvantage of this
approach is unjustified used indicators, their assessment is subjective. Thus, obtaining
the reliable data is a complicated task.
A flexible method for determining the cybersecurity level is reflected in [5]. The
authors propose to use cybersecurity metrics that can be used for evaluation, revision,
and improvement of the research entity cybersecurity level.
The approach is based on the metrics, which companies and organizations are using in
their business processes. To determine the new metric or specific measures, which
mathematically described, a set of relevant parameters should be identified. They can be
used to analyze and continually improve the business of an organization or a state. The
matrics usage is widely used nowadays. The disadvantages of this approach are
preliminary modeling, mathematical justification for the development and implementation
of these metrics, which is a difficult problem, also the result may not be unbiased enough.
A comprehensive method is proposed in [6].The cybersecurity level can be
determined by using completely independent metrics NSCI (National Cyber Security
Index) and ISD (Informational Society Score). NSCI consists of some sub-indexes:
ISD is divided on the following sub-indexes – IDI (The ICT Development Index) and
NRI (Networked Readiness Index) [7]. The disadvantage of this method is necessity
to allocate considerable resources for research due to the large number of indicators in
order to collect a reliable data.
Mathematical and statistical approach is described in [8]. It is a method for
assessing the cybersecurity level of CII assets, which allows to calculate a criticality
index of the entity. In order to implement the method, it is necessary to identify key
indicators, such as Severity level, the Availability of continuous operation systems,
Cost, Downtime etc. Thereafter, a weighting factor must be used for each determinant
indicator. Each of them must have a value from “0” to “100”, according to the
proposed scale of the value calculation. The mportance index of the CII entity can be
calculated based on the value of its criticality index. The disadvantage of this method
is a difficult adaption to the new systems and complexity of justifying weighting
factors and indicators.
In the next paper [9] was proposed a methodology for assessing ICT security,
using the example of automated banking systems, which is based on the concept
of a complex security management of those systems. The mentioned concept
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provides a mutually valid approach to select the most effective ways of achieving
the cybersecurity goals. Which also is taking into account the risk value at each
level of the management model. It makes it possible to comprehensively select
the alternative options for potential cybersecurity strategic decisions. However,
the proposed concept is focused exclusively on the banking sector and is not
flexible, which means it can not be used for other CII sectors.
According to [3], the analysis results of the mentioned approaches of determining the
cybersecurity level by the following criteria are reflected (Table 1): CS is consideration of
cybersecurity means and measures; ICT is consideration of ICT implementation; QP is
quantitative parameters; CIIP is CII sector protection; UM is universality [10-11].
Table 1. Cybersecurity level determination approaches
Criteria
CS ICT QP CIIP UM
Name
ICT Development index (ITU) + + + – –
Method Black, Scarfone, Souppaya + + + – –
NCSI (EGA) + + + – –
Nestruhin's method – – + + –
Yevseev's method + + + – –
The analysis shows that the existing methods have a list of disadvantages,
including unsubstantiated indicators, which are needed to develop metrics, complex
modeling of the given systems, the need to use a complex mathematical tools,
statistical resources involvement, which is needed for further analysis and creation of
the cybersecurity metrics. Given the need to assess the cybersecurity level of a CII
sector, a method for determining the cybersecurity level of the state’s CII sector needs
to be developed. This issue will be a main target of this work.
3 The main part of the study
A. Proposed method descryption
According to [3] the method of determining the cybersecurity level of the CII sector is
implemented in the following 3 stages:
1. Determination of metrics and cybersecurity index of a CII sector;
2. Determination of the ICT development and implementation metrics of a CII
sector;
3. Calculation of quantitative parameters, that describe the cybersecurity level of a
CII sector.
Input data: information regarding critical infrastructure, cybersecurity methods and
tools, ICT implementation.
Output data: quantitative parameters, that describe the security of a particular
industry or the state’s CII in general. Namely, cybersecurity metrics, ICT
development and implementation metrics, also a relevant cybersecurity index.
Consider in details each of the stage of the proposed method by itself.
Stage 1. Determination of the metrics and the cybersecurity index of a CII sector
Step 1.1. Formalization of the cybersecurity metrics
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Declaring a basic set of the cybersecurity metrics P :
n
P { Pi } {P1 , P2 , , Pn }, (1)
i 1
where Pi P (i = 1, n) is a metrics subset.
Based on the approach proposed in [12-13], the set (1) can be represented as a
linked list as it shown in Fig. 2:
P1 P2 ... Pn
head /
P
tail
Fig. 2. Representation of a basic set of cybersecurity metrics as a linked list
The set Pi can be represented as a subset system:
mi
Pi { Pij } {Pi.1 , Pi.2 , , Pi.mi }, (2)
j 1
where Pij (i = 1, n, j = 1, mi ) is metrics list of the i parameter (the metric’s range
value is determined according to appropriate standards and recommended practices
for each CII sector), mi is a number of metrics in i parameter.
Taking into account (2), the set (1) can be represented as follows:
n n mi
P { Pi } { { Pij }} {{P1.1 , P1.2 ,..., P1.m1 },
i 1 i 1 j 1 (3)
{P2.1 , P2.2 , ..., P2.m2 },..., {Pn.1 , Pn.2 ,..., Pn.mn }}, (i 1, n, j 1, mi ).
Step 1.2. The value calculation of the index, that describes the CII sector
cybersecurity level
The index of the CII sector cybersecurity level is calculated according to (1-3)
considering (4):
n mi
P ×100% ij
, max
Pij 0,
i=1 j=1
I CS = (4)
max
Pij
where max
Pij is the sum of the maximum possible values of Pij metric.
Stage 2. Determination of the ICT development and implementation metrics of a
CII sector
Step 2.1. Formalization of the ICT development and implementation metrics, that
describe the ICT readiness and availability
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Declaring the metrics set M , that describe the ICT development and
implementation:
q
M { M k } {M1 , M 2 ,, M q }, (5)
k 1
where Mk M (k = 1, q) is the subset of the ICT development and implementation
metrics, q is a number of the metric’s subsets. Similarly, taking into account [12-13],
the set (5) was represented as a linked list as it shown in Fig. 3.
M1 M2 ... Mn
head /
M
tail
Fig. 3. Representation of the ICT development and implementation metrics as a linked list
The set M k can be represented as a subset system:
pi
Mk { M kr } {M k .1 , M k .2 ,, M k . pi }, (6)
r 1
where M kr (k = 1, q, r = 1, pi ) are metrics of the set k , pi is metric’s number of the k
set.
Similarly, taking into account (6), the set (5) can be represented as:
q q pi
M { Mk } { { M kr }} {{M 1.1 , M 1.2 ,..., M 1. p1 },
k 1 k 1 r 1 (7)
{M 2.1 , M 2.2 , ..., M 2. p2 },..., {M q.1 , M q.2 ,..., M q. pq }}, (k 1, q, r 1, pi ).
Step 2.2. The value calculation of the ICT development and implementation metrics
The metrics, that describe ICT development and implementation in the CII sector
(7), can be calculated according to (8):
q pi
M ×100% kr
, M 0, q 0.
max
I DDL = k=1 r=1 (8)
q× M
max kr
kr
It should be noted, that since the metric M k can have different dimensions, at this
step, it also must be normalized using one of the known approaches.
Stage 3. Calculation of quantitative parameters, that describe the cybersecurity
level of a CII sector
Based on (4) and (8), it is possible to calculate the quantitative parameters (9), that
describe the cybersecurity level of a CII sector or a state:
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n mi q pi
P ×100% M ×100%
ij kr
, P 0, M 0, q 0. (9)
i=1 j=1 max max
I ratio = ICS - I DDL = - k=1 r=1
q× M
max max ij kr
Pij kr
B. An experimental study of proposed method in aviation
According to [3. 10, 11], an example of the developed method usage for the civil
aviation (CA) is showed below (Fig. 4). This sector includes to transportation and it is
a part of CI for most of states (Table 2).
Fig. 4. Civil aviation ICT communications scheme
Stage 1. Determination of the metrics and cybersecurity index of a CII sector
Step 1.1. Formalization of the cybersecurity metrics
Taking into account [12, 14], for the cybersecurity metrics, for n = 4,
m1 3, m2 4, m3 4, m4 1 the complete set of the cybersecurity metrics was
defined as follows:
4 4 mi
P ={ Pi } = { { Pij }} ={{PPLC ,PTHR ,PEDU },
i =1 i=1 j=1
{PBASS ,PESEV ,PEIDN ,PCIIP },{PCIRC ,PCRIS ,PCRIM ,PMIL },{PINT }} , (i = 1,n, j = 1,mi ).
Step 1.2. The value calculation of index, that describes the CII sector cybersecurity
level
According to (4):
(PPLC + PTHR + PEDU + PBASS + PESEV + PEIDN + PCIIP + PCIRC + PCRIS + PCRIM + PMIL + PINT ) 100
ICS = max max max max max max max max max max max max
35%.
PPLC + PTHR + PEDU + PBASS + PESEV + PEIDN + PCIIP + PCIRC + PCRIS + PCRIM + PMIL + PINT
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Table 2. Industries of CI in accordance to ENISA
Space&Research
Public admin.
Civil admin.
Industry
Transport
Finance
Energy
Health
Water
Food
ICT
EU state
Austria + + + + + + + + + -
Cyprus + + - + + - + + + -
Czech Rep. + + + - + + + + + -
Estonia + + + + + + + + + -
Finland + + + + + + + + - -
France + + + + + + + + + +
Hungary + + + + + + + + - -
Lithuania + + + + + + - + - -
Netherlands + + + - + + - + + -
Poland + + + + + + - + - -
Slovenia + + + + + + - + - -
Spain + + + + + + + + + +
Switzerland + + + + + + + + + -
UK + + + + + + - + - -
Stage 2. Determination of the ICT development and implementation metrics of a
CII sector
Step 2.1. Formalization of the ICT development and implementation metrics, that
describe the ICT readiness and availability
Based on [12, 15], for q 2, p1 3, p2 10 and considering (5-6), the set of ICT
development and implementation metrics can be shown as:
q q pi
M = { M k } = {{ { M kr }}= {{M ACC , M USE , M SKI },
k =1 k =1 r =1
{M POL , M INN , M RDN , M AFF , M BUS , M GOV , M SOC , M SKIL , M USE , M IMP }}, (k = 1,q, r = 1, pi ).
Step 2.2. The value calculation of the index, that describes the CII sector
cybersecurity level
According to (8) the index can be calculated as:
((M ACC + M USE + M SKI )
I DDL
max
( M ACC M USE
max max
+ M SKI )
( M POL + M INN + M RDN + M AFF + M BUS + M GOV + M SOC + M SKI + M USE + M IMP )) 100%
max max max max max max max max max max
62,5%
( M POL + M INN + M RDN + M AFF + M BUS + M GOV + M SOC + M SKI + M USE + M IMP ))
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Stage 3. Calculation of the quantitative parameters, that describe the cybersecurity
level of CA
Based on the results in Step 1.2, Step 2.2 and considering (9), quantitate
parameters, that describe the cybersecurity level of CA can be calculated as follows:
I ratio = ICS - I DDL 35 % 62,5 % 27,5 %.
The difference between I CS and I DDL indicators shows the correlation between the
cybersecurity level and ICT development and implementation index. A positive result
shows that cybersecurity level meets a sufficient level of ICT index for CA (or even
overcame it), on the other hand, a negative result shows that cybersecurity level is not
sufficient for current ICT index. The obtained result I ratio = 27,5 % for CA shows that
cybersecurity level should be improved.
4 Conclusion and future research study
Consequently, in this paper the modern methods, tools for assessing the cybersecurity
level and their supporting instruments were analyzed. The research found that currently
there are no comprehensive, flexible methods that can quantify the cybersecurity level
of the CII sector. A method for determining the cybersecurity level has been developed.
This method provides the sets of cybersecurity level and ICT development and
implementation metrics in a linked lists view, also helps to calculate its relevant metrics.
It allows to determine quantitative parameters, that describe the cybersecurity level of a
particular industry or the state’s CII in general. The developed method can be used to
analyze a particular state’s CII, determine the cybersecurity level, identify critical
systems, which need to be protected from external and internal threats. For example, the
proposed method can be applied for comparing the cybersecurity level before and after
the implementation of certain ICT security measures.
References
1. S. Gnatyuk, “Critical Aviation Information Systems Cybersecurity”, Meeting Security
Challenges Through Data Analytics and Decision Support, NATO Science for Peace and
Security Series, D: Information and Communication Security. IOS Press Ebooks, vol.47,
№3, рр. 308-316, 2016.
2. The Law of Ukraine “On Basic Principles of Cyber Security of Ukraine” of 15.10.2017,
№2163-VIII, Available Online, URL: http://zakon3.rada.gov.ua/laws/show/2163-19
3. V. Sydorenko, A. Polozhentsev, S. Gnatyuk, “The method of determining the security
level of the critical information infrastructure”, Academy of Engineering of Ukraine News,
vol. 42, pp. 81- 89, 2017 (in Ukrainian).
4. Global Cybersecurity Index, Available Online, URL: https://www.itu.int/en/ITU-D/.
5. P. Black, K. Scarfone, M. Souppaya, “Cyber security metrics and measures”, Wiley
Handbook of Science and Technology for Homeland Security, vol. 4, 2010.
6. National Cyber Security Index, Available Online, URL: http://ncsi.ega.ee/ncsi-index/.
7. Network Readiness Index 2016. Available Online, URL: http://www3.weforum.org/
docs/GITR/2014/GITR_OverallRanking_2016.
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0). COAPSN-2020: International Workshop on
Control, Optimisation and Analytical Processing of Social Networks
8. A. Nestrugin, Technique of automatic ranking of objects protection according to the level of
potential danger on the example of oil refineries, pp. 77-84, 2014 (in Russian).
9. S. Evseev, “Methodology of Information Security Assessment of Automated Banking Systems
of Ukraine”, Information Security, vol. 22, No.3, pp. 297-309, 2016 (in Ukrainian).
10.Gnatyuk S., Polishchuk Yu., Sydorenko V., Sotnichenko Yu. “Determining the level of
importance for critical information infrastructure objects”, Proceedings of 2019 IEEE
International Scientific-Practical Conference: Problems of Infocommunications Science
and Technology, PIC S and T 2019, Kyiv, Ukraine, October 8-11, 2019, pp. 829-834.
11.R. Odarchenko, V. Gnatyuk, S. Gnatyuk, A. Abakumova, Security Key Indicators
Assessment for Modern Cellular Networks, Proceedings of the 2018 IEEE First
International Conference on System Analysis & Intelligent Computing (SAIC), Kyiv,
Ukraine, October 8-12, 2018, pp. 1-7.
12.T. Kormen, C. Leiserson, R. Rivest, C. Stein, “Algorithms: Construction and Analysis, 3rd
Edition”, Moscow: LTD Williams, 1328 p., 2013.
13.Smirnov O., Kuznetsov A., Kiian A., Zamula A., Rudenko S., Hryhorenko V., “Variance
Analysis of Networks Traffic for Intrusion Detection in Smart Grids”, 2019 IEEE 6th
International Conference on Energy Smart Systems, Kyiv, Ukraine, 2019, P. 353-358.
14.Zhukov I.A. “Implementation of integral telecommunication environment for harmonized
air traffic control with scalable flight display systems”, Aviation, 2010, №14 (4), 117-122.
15.Smirnov O., Kuznetsov A., Kavun S., Babenko B., Nakisko O., Kuznetsova K., “Malware
Correlation Monitoring in Computer Networks of Promising Smart Grids’, 2019 IEEE 6th
International Conference On Energy Smart Systems, Kyiv, Ukraine, 2019 P. 347-352.
16.Boyko N., Pylypiv O., Peleshchak Y., Kryvenchuk Y., Campos J.: Automated document
analysis for quick personal health record creation. 2nd International Workshop on
Informatics and Data-Driven Medicine. IDDM 2019. Lviv. p. 208-221. (2019)
17.Kryvenchuk Y., Mykalov P., Novytskyi Y., Zakharchuk M., Malynovskyy Y., Řepka M.:
Analysis of the architecture of distributed systems for the reduction of loading high-load
networks. Advances in Intelligent Systems and Computing. Vol.1080. p.759-550. (2020)
18.Kryvenchuk Y.,Vovk O., Chushak-Holoborodko A., Khavalko V., Danel R.: Research of
servers and protocols as means of accumulation, processing and operational transmission
of measured information. Advances in Intelligent Systems and Computing. Vol.1080.
p.920-934. (2020)
19.Mishchuk O., Tkachenko R., Izonin I.: Missing Data Imputation through SGTM Neural-
like Structure for Environmental Monitoring Tasks. Advances in Computer Science for
Engineering and Education. ICCSEEA2019. Advances in Intelligent Systems and
Computing. Springer, Cham, 2019, pp. 142-151
20.Fedushko S., Ustyianovych T.: Predicting Pupil’s Successfulness Factors Using Machine
Learning Algorithms and Mathematical Modelling Methods. Advances in Intelligent
Systems and Computing series, ICCSEEA 2019, AISC 938, vol. 938, pp. 625-636 (2020).
https://doi.org/10.1007/978-3-030-16621-2_58
21.Korobiichuk I., Fedushko S., Juś A., Syerov Y.: Methods of Determining Information
Support of Web Community User Personal Data Verification System. Automation 2017.
ICA 2017. Advances in Intelligent Systems and Computing, vol. 550, pp. 144-150.
Springer (2017). https://doi.org/10.1007/978-3-319-54042-9_13
22.Korobiichuk I., Syerov Y., Fedushko S.: The method of semantic structuring of virtual
community content. Advances in Intelligent Systems and Computing, vol. 1044, pp. 11-18
(2020). https://doi.org/10.1007/978-3-030-29993-4_2
23.Shakhovska N., Shakhovska K., Fedushko S.: Some Aspects of the Method for Tourist
Route Creation. Proceedings of the International Conference of Artificial Intelligence,
Medical Engineering, Education (AIMEE2018). Advances in Artificial Systems for
Medicine and Education II series, vol. 902, pp. 527-537 (2020)
https://doi.org/10.1007/978-3-030-12082-5_48
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