Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue

Abstract

Digital trends like blockchain have led to cryptocurrency payments becoming popular in e-commerce. While cryptocurrencies have benefited users, they have also attracted criminals who use them to commit cyberattacks and harm security. In this research paper, we present an analysis of the following factors that can strongly influence the development of the cryptocurrency environment and be associated with cryptocurrency-related crime at the national level: GDP, digital development, e-commerce market size, the level of mass adoption of cryptocurrency, the level of national cybersecurity, and fraud in cryptocurrency crime for selected countries worldwide. By applying correspondence analysis, we constructed visually intuitive models based on assessments from the global data and business intelligence platform and official statistical reports. We have established a fairly strong positive correlation between fraud in cryptocurrency crime and digital development, e-commerce market size, and the level of mass adoption of cryptocurrency; a fairly strong negative correlation between the level of fraud in cryptocurrency crime and the level of cybersecurity in a specific country; a fairly strong positive correlation between the level of mass adoption of cryptocurrency and the level of cybersecurity. The proposed models give decision-makers a clear understanding of the key factors in cryptocurrency that pose a high risk of related crime.

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O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
VOLUME XX, 2024 1
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2022.Doi Number
Cryptocurrency Crime Risks Modeling:
Environment, E-Commerce, and Cybersecurity
Issue
Olha Kovalchuk1, Ruslan Shevchuk2,3, (Member, IEEE) and Serhiy Banakh4
1Theory of Law and Constitutionalism Department, West Ukrainian National University, 46009 Ternopil, Ukraine
2Department of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland
3Department of Computer Science, West Ukrainian National University, 46009 Ternopil, Ukraine
4Department of Criminal Law and Process, West Ukrainian National University, 46009 Ternopil, Ukraine
Corresponding author: Ruslan Shevchuk (rshevchuk@ubb.edu.pl).
ABSTRACT Digital trends like blockchain have led to cryptocurrency payments becoming popular in e-
commerce. While cryptocurrencies have benefited users, they have also attracted criminals who use them to
commit cyberattacks and harm security. In this research paper, we present an analysis of the following factors
that can strongly influence the development of the cryptocurrency environment and be associated with
cryptocurrency-related crime at the national level: GDP, digital development, e-commerce market size, the
level of mass adoption of cryptocurrency, the level of national cybersecurity, and fraud in cryptocurrency
crime for selected countries worldwide. By applying correspondence analysis, we constructed visually
intuitive models based on assessments from the global data and business intelligence platform and official
statistical reports. We have established a fairly strong positive correlation between fraud in cryptocurrency
crime and digital development, e-commerce market size, and the level of mass adoption of cryptocurrency; a
fairly strong negative correlation between the level of fraud in cryptocurrency crime and the level of
cybersecurity in a specific country; a fairly strong positive correlation between the level of mass adoption of
cryptocurrency and the level of cybersecurity. The proposed models give decision-makers a clear
understanding of the key factors in cryptocurrency that pose a high risk of related crime.
INDEX TERMS Cryptocurrency, cyber-crime, e-commerce, cybersecurity, risk modeling
I. INTRODUCTION
The rapid progress of Information Technologies (IT) and
their widespread implementation in critical spheres of
societal existence have led to the gradual transition of
modern civilization into a digital dimension. Today,
humanity utilizes digital world networks for
communication, management, entertainment, trade, and
transactions. In 2023, the cryptocurrency market (a form of
digital currency) reached three trillion dollars, with
thousands of different cryptocurrencies in circulation. The
cryptocurrency ecosystem has become the infrastructure of
the digital business ecosystem [1, 2]. According to trading
platforms and network wallets, the overall number of
cryptocurrency users worldwide nearly doubled from 2018
to 2020, and this growth accelerated in 2022.
Cryptocurrencies were adopted in 56 countries worldwide
from 2019 to 2023 [3].
Recently, there has been widespread adoption of
cryptocurrency in electronic commerce. Large e-commerce
platforms and online stores favor cryptocurrencies for their
ability to facilitate fast and secure global transactions. The
anonymity and decentralization of cryptocurrencies offer
users advantages but also pose risks to individual digital
sovereignty and privacy [4, 5], making the world
unpredictable and fragmented. Global security increasingly
depends on the digital realm's security [6]. Criminals
leverage the benefits of the digital society, particularly
anonymity and IT advancements, to commit cybercrimes.
The number of cyber threats has increased globally in 2023
[7]. According to "The Global Risks Report 2023,"
widespread cybercrime and cyber insecurity rank eighth in
the list of global risks by severity over the next two and ten
years [8]. Cybercriminals increasingly utilize
cryptocurrencies to commit offenses and launder proceeds,
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

2 VOLUME XX, 2024
as the movement of money in the blockchain is difficult to
trace, and transactions are shrouded anonymously [9].
Only in 2021, criminals stole $3.2 billion in cryptocurrency
most of which was stolen from decentralized finance (DeFi
platforms). These organizations allow users to conduct
financial transactions without intermediaries using
cryptocurrency and blockchain networks. In this case, there
is no need to involve centralized exchanges or other
organizations that provide financial services, such as banks.
One example of DeFi is U.S. Patent 11,170,437
“Blockchain-based financing” ‒ a computer-implemented
supply chain method where a blockchain core node obtains
a financing transaction with account receivable information
from a light node. The core node determines financing
terms like grant limit, repayment period, and interest rate
based on the receivable information and publishes a smart
contract with these terms to the blockchain ledger. The light
node can then invoke this smart contract for financing
management operations [10].
Another example is the “Blockchain-based method and
device for distributing copyright income of works”. It
involves detecting blockchain transactions corresponding
to the usage of a copyrighted work. A smart contract
executes to distribute copyright earnings to the appropriate
earners based on the usage event and rights information
published on the blockchain. In essence, it automatically
triggers smart contracts to pay copyright holders when their
works are used, based on the blockchain rights data [11].
Undoubtedly, DeFi provides its users with significant
advantages, such as accessibility, transparency,
composability, self-custody, high interest rates, and 24/7
operability. However, these financial services and
platforms pose substantial dangers for committing
cryptocurrency crimes, creating a friendly environment for
crimes related to cryptocurrency.
The year 2021 saw a staggering 1330% surge in losses from
thefts targeting DeFi platforms when compared to the prior
year. The scale of these losses eclipsed those from
centralized exchanges by a factor of six [12].
One of the most significant cyber-attacks in 2023
recognizes cloud exploitation. With the widespread
adoption of this technology, cybercriminals use forged
identities or stolen credit cards to take advantage of free
offers to mine cryptocurrencies provided by cloud service
providers [13]. Cybercrimes, particularly hacking and
ransomware, are associated with cryptocurrencies [14-16].
This further complicates the investigation of such crimes,
as there is currently no legislation regarding the seizure of
virtual assets. Additionally, the governments of certain
states may adopt a lenient or even supportive stance
towards the illegal use of cryptocurrencies for malicious
purposes.
Cryptocurrency-related crime is rapidly spreading
worldwide and has already become a transnational
problem. It has impacted the most critical aspects of
societal life, including not only electronic commerce but
also the financial security of individual users and
businesses, cybersecurity, and national security overall.
Therefore, applied research on identifying significant
factors correlated with elements of the cryptocurrency
ecosystem and assessing the risks of the dynamic
cryptocurrency environment for the economies,
cybersecurity, and security of countries worldwide is
crucial. Such knowledge can provide reliable information
for decision-makers in developing informed decisions to
ensure legal regulations for the cryptocurrency market,
enhance the security of the cryptocurrency environment,
and improve the cybersecurity of countries worldwide.
This research aims to identify subtle correlations between
various factors associated with the cryptocurrency
environment and their impact on the level of cybersecurity
in countries worldwide.
This study analyzes key factors that can significantly
influence the cryptocurrency environment's evolution and
link to crypto-related criminal activities nationwide.
Through correspondence analysis, we developed
straightforward visual models informed by data from a
global business intelligence platform and official statistical
reports. The findings reveal a fairly strong positive
correlation between fraudulent cryptocurrency crimes and
factors like digital development progress, the size of the e-
commerce market, and widespread cryptocurrency
adoption levels. Conversely, a fairly strong negative
correlation emerges between fraudulent cryptocurrency
crime rates and a country's cybersecurity readiness.
Interestingly, there is also a fairly strong positive
correlation between widespread cryptocurrency adoption
and heightened cybersecurity measures in place. The
models elucidate these intricate relationships, providing
valuable insights for stakeholders navigating the
cryptocurrency landscape while mitigating associated risks.
The article is divided into five sections. The first section
provides a brief overview of the current state of
cryptocurrency-related crime and highlights the need for
effective measures to prevent the proliferation of
cryptocurrency crime threats across the security domains of
countries worldwide. The second section reviews analytical
studies on identifying complex and ambiguous
relationships between components of the cryptocurrency
ecosystem and factors contributing to the risk of
cryptocurrency crime. In the third section, we analyze the
primary risk factors and threats posed by the widespread
adoption of cryptocurrencies to the personal security of
citizens, the financial security of individual users and
businesses, electronic commerce, and cybersecurity in
countries worldwide. We describe the dataset and justify
the chosen method for conducting empirical research. The
fourth section presents the results of applying
correspondence analysis to evaluate cryptocurrency crime
risks and assess the quality of decisions. In the fifth section,
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
VOLUME XX, 2024 3
we conclude the study and discuss its limitations and
potential avenues for future research.
II. LITERATURE REVIEW
The examination of complex and ambiguous relationships
between components of the cryptocurrency ecosystem and
factors contributing to the risk of cryptocurrency crime,
particularly imperfections in legal regulation, has been the
focus of specific scientific and analytical research [17]. M.C.
Șcheau et al. studied the risks associated with the adoption of
cryptocurrency in the economy and everyday life [18]. They
asserted a clear connection between cybercrime and
cryptocurrencies. Therefore, authorities should harmonize
regulatory acts in the field of cryptocurrencies with those in
the field of cybercrime, and decision-making concerning
cryptocurrency should be clear and based on universally
recognized principles.
S. Kapoor demonstrated a positive correlation between the
growth of online businesses and the number of committed
cybercrimes [19]. C. Davison et al. provided a decision
support system using analytic models to recommend an
appropriate digital currency exchange platform [20]. The
proposed analytic model can offer additional information to
the general public about digital currency exchange platforms
and simplify investment in trading.
D. Sanz-Bas et al. investigated the risks associated with using
cryptocurrencies for the economy, especially money
laundering [21]. L.P. Krishnan et al. analyzed key concepts of
blockchain technology and cryptocurrencies, examined fraud
characteristics, and explored how machine learning models
use these characteristics to detect scams. Through
performance analysis, the authors identified the most effective
models, datasets, and features for various scam types [22].
K. Zhao et al. created a self-supervised model to determine
which cryptocurrency transactions are legitimate and comply
with laws and regulations and which are used for illegal
activities [23]. E. Ilbiz et al. examined the compatibility of the
Global Conference on Criminal Finances and
Cryptocurrencies with a sharing economy model. They
classified these conferences as a "partial" sharing economy
platform, reducing transaction costs for governmental and
private entities, promoting knowledge exchange on the latest
trends and threats related to cryptocurrency laundering, and
reducing transaction costs for networking [24].
J. Liu et al. proposed an improved graph embedding algorithm
specifically for money laundering detection, which
comprehensively considers behavioral models of money
launderers and structural information of transaction networks
and can automatically obtain features of money laundering
addresses [25]. C. Watters applied a comparative approach to
study the issue of establishing criminal jurisdiction for crimes
related to cryptocurrency [26]. However, the challenges of
identifying key risk factors for cryptocurrency crimes and
correlating factors within the cryptocurrency environment are
insufficiently explored and require additional
multidimensional analysis.
III. MATERIALS AND METHODS
A. THE RISK OF THE CRYPTOCURRENCY: THE
DANGERS OF THE BENEFITS
Recently, the popularity of cryptocurrency (a digital or virtual
payment system secured by cryptography, preventing forgery)
has been rapidly increasing. It is a peer-to-peer ecosystem that
enables anyone, anywhere, to send or receive money using
digital wallets or exchanges. As cryptocurrency operates
outside traditional financial institutions, it can bypass banks
for payment verification, decentralizing transactions and
placing responsibility on its users. Cryptocurrency is a digital
(virtual) asset used for payment on the Internet. It is based on
complex encryption, providing anonymous users with the
ability to send or receive digital (virtual) money directly to
each other using digital wallets or digital exchanges [27].
Well-known brands, popular marketplaces, and leading
payment systems actively use this digital payment method,
and retail sellers in many countries adopt it [28]. The
cryptocurrency world is evolving and has great potential to
offer users comfortable services daily. However, its
advantages, such as anonymity and the ease of transferring
large sums of money, are attractive to cybercriminals for
carrying out cyberattacks. Due to the absence of government
regulation, cryptocurrency is increasingly becoming a tool for
cybercrime, posing a significant threat to its users.
Current data protection technologies do not secure owners
from information leaks. Even cybercriminal ransom demands
today often involve cryptocurrency. This guarantees them the
ability to intervene remotely, ensuring anonymity and relative
security. The inability to trace fund movements complicates
the process of identifying criminals by law enforcement. For
instance, the Hong Kong-based cryptocurrency exchange
platform CoinEx lost $ 70 million in cryptocurrency due to a
cyber-attack in September 2023. The breach in 2023 of the
account of the co-founder of the decentralized blockchain
Ethereum and the cryptocurrency Ether made an NFT
phishing attack possible, resulting in losses exceeding $
691,000. In November 2023, cryptocurrency worth 26 million
dollars was stolen from the Kronos Research system due to
unauthorized access, leading to the suspension of trading. In
the same month, a cyberattack resulted in the withdrawal of $
54.7 million of KyberSwap users' cryptocurrency to the
attackers' wallets.
The presence of a fake Ledger Live app for cryptocurrency in
the Microsoft Store resulted in users losing at least $ 768,000
in crypto. Fake accounts posing as blockchain security
organizations generate $ 50,000 a day [29]. Cybercriminals
refine their methods and tools parallelly with IT development.
Anonymous cryptocurrency mining, or cryptojacking
(unauthorized use of users' electronic devices for
cryptocurrency mining without installing any software), is a
popular cybercrime technique [30].
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

4 VOLUME XX, 2024
According to "The 2023 Crypto Crime Report" by
Chainalysis, cryptocurrency-related crime set a record in
2022. The volume of illegal transactions with cryptocurrency
reached $20.1 billion compared to $18 billion in 2021. These
estimates are only preliminary and do not take into account the
profits from non-cryptocurrency crime where cryptocurrency
was used as a form of payment. For example, revised estimates
for 2021 were $4 billion higher than previous figures [12].
Almost 15 years ago, the official recognition of Bitcoin as a
digital currency and payment system took place. Since then,
the use of cryptocurrency has rapidly spread worldwide.
However, along with the benefits for its users, transparency
and uncontrolled cryptocurrency transactions have provided
criminals with the opportunity to use them as fake internet
money, particularly in the Deep Web for transactions related
to drugs, weapons, pornography, and other prohibited goods.
Cryptocurrency crime expands with the growth of the
cryptocurrency market, asserting its criminal reputation. It is
impossible to determine a typical criminal profile for a crypto-
criminal: it can be anyone, from a novice teenager to groups
of experienced hackers.
However, some countries have much more developed
cryptocurrency crimes than others. Therefore, research on
detecting subtle correlations between different factors
associated with cryptocurrencies and their impact on the level
of cybersecurity worldwide is relevant.
According to "The 2022 Crypto Crime Annual Report", the
top 10 countries for cryptocurrency crime in 2022 (fraud by
totals) are as follows: North Korea, United States, Russia,
China, United Kingdom, Japan, Hong Kong, Canada, British
Virgin Islands, and Seychelles [31]. However, one fraud case
can have varying numbers of victims, from one to several
thousand, and cause different incomparable amounts of losses.
Therefore, it is impossible to provide objective assessments of
the level of cryptocurrency crime in a specific country (Figure
1).
FIGURE 1. The bubble chart of the Number of Fraud Cases, Fraud Totals
($ thousand), and level of cryptocurrency crime (size of the bubbles is
inversely proportional to Rank) for the Top 10 Countries for
cryptocurrency crime in 2022.
For example, North Korea, which is first in the top countries
by fraud cases, is only fourth for fraud totals. In Turkey, for
2022, only one case was recorded, causing colossal losses of
2 billion dollars. However, this fact is not evidence of the
existence of a criminal ecosystem in this country [32].
The preliminary graphical analysis (Figure 2, Table I) allows
us to conclude that there is no obvious correlation between
GDP and fraud cryptocurrency crime totals. Therefore, we
can assume that cryptocurrency crime is spreading regardless
of the country's level of economic development. To identify
non-obvious factors associated with cryptocurrency crime,
other, specific methods of analysis should be applied.
TABLE I
GDP, AND FRAUD TOTALS OF COUNTRIES IN THE WORLD
Country
GDP
Fraud totals
AUS
Australia
1610556
135
BRA
Brazil
1645837
370
CAN
Canada
2015983
169729
CHE
Switzerland
810830
32000
CHN
China
16862979
2269521
CZE
Czech Republic
276914
161
DNK
Denmark
396666
600
ESP
Spain
1439958
1000
FIN
Finland
296016
31
GBR
United Kingdom
3108416
602011
GNQ
Estonia
36039
2000
GRC
Germany
4230172
3000
HKG
Hong Kong
369722
586002
IND
India
2946061
3000
ISR
Israel
467532
12000
ITA
Italy
2120232
195303
JPN
Japan
5103110
1241044
KOR
South Korea
1823852
71000
MLT
Malta
16695
3000
NOR
Norway
445507
1000
POL
Poland
655332
29352
PRK
North Korea
15847
1597365
RUS
Russia
1647568
1456900
SVK
Slovakia
116748
5000
TUR
Turkey
795952
2000000
USA
United States
22939580
2045349
MN
Mongolia
14280
4
PA
Panama
60121
51
KY
Cayman Island
6256
245
BZ
Belize
1909
342
VN
Vietnam
368002
500
IE
Ireland
516253
570
KN
Saint Kitts and Nevis
976
687
GI
Gibraltar
2344
2000
MH
Marshall Islands
241
6167
NZ
New Zealand
247640
12439
SG
Singapore
378645
45123
VU
Vanuatu
999
45253
IO
British Virgin Islands
1288
65066
SC
Seychelles
1288
65066
Cryptocurrency crime in different countries has its own goals.
For example, professional hacker groups in North Korea,
which rank first in the ranking of cryptocurrency criminals,
target their cyberattacks mainly at government and private
organizations worldwide. Cryptocurrency criminals in the
United States, which is second in the ranking, stand out for its
large underground cryptocurrency economy and massive
high-profile frauds. Russian organized crime, supported by the
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
VOLUME XX, 2024 5
state leadership, specializes in ransomware and attacks the
whole world, ranking third in the ranking of cryptocurrency
criminals.
FIGURE 2. The chart relationship 2022 GDP ($ million) and 2022 fraud
crypto crime totals ($ thousand) for individual countries worldwide.
China ranks fourth in the list of cryptocurrency crimes, thanks
to large-scale fraud, Ponzi schemes, and exchange hacks. The
United Kingdom ranks fifth among the countries with the
largest number of cryptocurrency criminals. Large-scale
money laundering in this country has become possible due to
overly lenient company registration laws, which provide
opportunities for legitimizing fraud [31].
B. THE RISK OF CRYPTOCURRENCY IN E-COMMERCE
One of the current trends in the cryptocurrency world is the
growth of cryptocurrency payments in e-commerce. It, like
cryptocurrencies, uses an online environment and does not
require a physical representation of participants (traders and
buyers). The global spread of Internet services and the
introduction of digital payments have contributed to the rapid
development of e-commerce business. The estimated value of
the global e-commerce market exceeds $6 trillion [33]. It is
expected to reach $6.3 trillion by the end of 2023 [34].
In the top ranking of the world's countries in terms of e-
commerce market size, China remains in first place in 2023.
According to forecasts, e-commerce sales in this country will
exceed $ 3 trillion. The United States secured the second place
in this ranking, with its e-commerce market totaling almost 1.2
trillion dollars. The other 8 positions in the Top-10 expected
e-commerce revenue in 2023 are occupied by the following
countries with the corresponding total e-commerce revenue
there [33]:
 United Kingdom ‒ $ 196 billion;
 Japan ‒ $ 193.4 billion;
 South Korea ‒ $ 147.4 billion;
 India ‒ $ 118.9 billion;
 Germany ‒ $ 97.3 billion;
 Indonesia ‒ $ 97.1 billion;
 Canada ‒ $ 82.8 billion;
 France ‒ $ 79.4 billion.
The five of these countries (United States, China, United
Kingdom, Japan, and Canada) are also included in the top 10
countries for cryptocurrency crime in 2022 [31]. However, a
direct visual analysis of the e-commerce market size,
cryptocurrency crime rank (fraud totals), and the level of
cryptocurrency crime does not give grounds to assert that the
e-commerce market size correlates with the level of
cryptocurrency crime in a particular country (Figure 3).
Therefore, more research is needed to identify the real
relationships between these estimates.
FIGURE 3. The bubble chart of the e-commerce market size ($ billion),
cryptocurrency crime (fraud totals in $ billion), and level of
cryptocurrency crime (size of the bubbles is inversely proportional to
the rank) for selected countries.
According to preliminary estimates of experts, in 2023, the
revenue in the e-commerce market will reach $ 3099 billion,
the largest of which will be in China - $ 931.1 billion. User
engagement in the eCommerce market will reach 48.7%, with
an average income of $ 1308 per user. The market segments
of modern e-commerce are changing. Recently, the consumer-
to-consumer (C2C) model has become increasingly popular as
opposed to business-to-business (B2B) and business-to-
consumer (B2C). Global e-commerce is increasingly moving
from online platforms to mobile applications [35].
Cryptocurrencies are becoming increasingly popular in e-
commerce. Their use reduces the cost of transactions, speeds
up cross-border transactions, increases security, and expands
market access.
As the e-commerce market has grown in recent years, so has
the incidence of e-commerce fraud. In 2022, the e-commerce
industry lost $41 billion, or 2.9% of its global revenue, to e-
commerce fraud. Online retailers spent 10% of their total
annual revenue on fraud prevention. 75% of online shoppers
have committed fraud. In 2023, online payment fraud losses
in e-commerce (sales of digital/physical goods, money
transfers, banking transactions, and purchases) are expected to
exceed $48 billion [36]. The most common type of
eCommerce marketplace fraud in 2022 was phishing attacks.
They affected 43% of online sellers [37]. Account takeover
fraud caused a loss of $11.4 billion in 2022 [38].
The rapid growth of e-commerce has led to an expected
increase in online security threats. The e-commerce
environment, despite the cryptographic protection and
FINAUS
CZE
BRA
DNK NOR
ESP
GNQ
MLT
IND
GRC
SVK
ISR
POL CHE
KOR
CAN
ITA
HKGGBR
JPN
RUS
PRK
TUR USA
CHN
-1000,000
0,000
1000,000
2000,000
3000,000
-10000000 010000000 20000000 30000000
Fraud totals
GPD
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

6 VOLUME XX, 2024
claimed security of blockchain payments, is characterized by
a unique risk of fraud. Hackers are increasingly choosing
blockchain technology to steal personal data, launder money,
and carry out complex online payment fraud. The absence of
regulatory measures on eCommerce platforms creates an
illusion of impunity for cybercriminals and increases the risk
of fraud for all participants in electronic payments. Online
business is increasingly becoming a target for hackers. This
exacerbates e-commerce security risks, including the leakage
of personal and financial data of users, credit card information,
etc. In 2023, e-commerce security recognizes phishing attacks
as the biggest threat. The theft of confidential information has
taken on new forms and has become extremely targeted and
disguised [39]. The second place in the e-commerce security
threats ranking in 2023 is occupied by ransomware attacks, as
a result of which cybercriminals demand a ransom in
cryptocurrency [40].
Cybercriminals are becoming increasingly organized and
professional. They look for the vulnerabilities of the victim
and use psychological influence. The anonymity and
unregulated use of the dark web and cryptocurrencies create a
favorable landscape for the growth of the criminal economy,
including the involvement of previously law-abiding people
from countries with low socio-economic development in fraud
[41].
The COVID-19 pandemic has led to an increase in e-
commerce users and online payments. In 2020, online
payment fraud accounted for 38% of total fraud worldwide.
This was 14% more than before the pandemic. In 2022, losses
from digital payment fraud in eCommerce amounted to more
than $ 40 billion [42]. To increase consumer protection and
prevent digital payment fraud, innovations in the field of
online payments and lending are expanding, including
cryptocurrency assets, BNPL (Buy Now Pay Later), digital
wallets, neobanks, and other forms of alternative payment
methods. However, the introduction of innovations requires
updating existing regulations in the field of digital payments
and improving legislation in this area [43].
C. RISKS OF CRYPTOCURRENCIES FOR NATIONAL
CYBERSECURITY
As of 2023, individuals globally own about 4.2% of the total
crypto. More than 420 million people use cryptocurrencies
around the world [32]. The level of cybersecurity of a country
depends on the level of cryptocurrency adoption [18].
Residents of wealthy countries with advanced digital
technologies do not always prefer to invest their savings in
cryptocurrencies. The Global Crypto Adoption Index (GCAI)
was developed to identify countries with massive
cryptocurrency adoption, where the vast majority of citizens
invest their wealth in cryptocurrencies. The GCAI is
calculated for 155 countries based on the following 5 measures
[32]:
1) Centralized service value received ranking is a
measure of the total amount of cryptocurrencies
received through centralized services, taking into
account the country's wealth per capita (PPP per
capita).
2) Retail centralized service value received ranking is a
measure of the amount of cryptocurrency received by
individual, non-professional users in retail-sized
transactions (under $ 10,000) at centralized services
in each country.
3) P2P exchange trade volume ranking is a weighted
measure of P2P trade volume in each country. It takes
into account PPP per capita and number of internet
users.
4) DeFi value received ranking estimates the volume of
DeFi transactions carried out by users using DeFi
(decentralized finance) protocols, considering the
level of PPP per capita in the country.
5) Retail DeFi value received ranking estimates the
volume of DeFi transactions made in retail transfers,
considering PPP per capita.
All GCAI dimensions are rankings of 154 countries. Each of
them is weighted by population and purchasing power, the
geometric mean of each country's ranking in all five categories
is calculated, and the resulting Index score is normalized on a
scale from 0 to 1. The closer the Index score is to 1, the higher
the country's Index rank is in the overall GCAI ranking. The
country with the highest Index score of 1 among all countries
receives an Index rank of 1; the country with the lowest Index
score of 0 corresponds to an Index rank of 155. The main
disadvantage of the GCAI is that it assigns different Index
ranks to countries with the same Index score [32]. This fact
significantly complicates direct applied analysis and requires
the use of specific research methods.
An important indicator of national security in the modern
digital society is the level of state cyber defense [8, 44]. The
National Cyber Security Index (NCSI), developed by the e-
Governance Academy Foundation, is traditionally used to
assess it [45]. This is an international dynamic measure of the
effectiveness of countries' actions to prevent cyber threats and
eliminate the consequences of cyber incidents. The NCSI
2023 is another attempt to develop an effective,
comprehensive tool for analyzing and measuring the level of
development of national cybersecurity in countries around the
world.
The NCSI is based on the following indicators of the national
cybersecurity system [45]:
 unavailability of electronic services;
 violation of data integrity;
 violation of information confidentiality.
These threats affect the quality of functioning of information
and communication systems in countries worldwide and the
provision of electronic services to the population, including e-
commerce.
The NCSI is a measure of the effectiveness of the
government's actions to ensure national cybersecurity and
takes into account the following aspects:
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
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VOLUME XX, 2024 7
 peculiarities of national legislation: laws, regulations,
legal acts in the field of cyber defense, etc;
 national organizations, agencies, and departmental units
that deal with the issue of protection against cyber
threats;
 effectiveness and development of the national policy on
the cyber defense of the state, use of information
technologies, information resources, and software.
NCSI includes 3 categories, 12 capacities, and 46 indicators.
An expert group determines the weight of each indicator.
Country ratings are based on open data from legal acts, official
documents, and websites.
One of the factors affecting the level of cyber defense of
countries is the level of use of public electronic networks and
public electronic services [46]. The Digital Development
Level (DDL) is an aggregate indicator of the E-Government
Development Index (EGDI) and Networked Readiness Index
(NRI) [45]:
DLL = (EGDI%+NRI%)/2. (1)
Data collection and analysis, as well as the justification of
country results, is ongoing. The country ranking is not annual,
and the NCSI assessment methodology is constantly being
improved and requires additional research.
The plot of the dependence of the National Cyber Security
Index 2023, Digital Development Level 2023 of 40 countries
(data from 01.09.2023) [45], and the Global Crypto Adoption
Index 2023 [32] does not give a clear picture of the real
relationships between these indicators (Figure 4, Table II).
FIGURE 4. A bubble chart of the DDL, NCSI, and GCAI (size of the bubbles) for individual countries of the world.
TABLE II
GCAI, NCSI, AND DDL OF COUNTRIES IN THE WORLD
Country
GCA
NCSI
DDL
AFG
Afghanistan
147
12,99
19,50
ALB
Albania
85
62,34
48,74
DZA
Algeria
47
33,77
42,81
ATG
Antigua and Barbuda
145
11,69
57,10
ARG
Argentina
15
63,64
60,43
ARM
Armenia
89
35,06
55,06
AUS
Australia
41
66,23
77,61
AUT
Austria
84
85,71
75,76
AZE
Azerbaijan
74
63,64
54,78
BHS
Bahamas
127
20,78
65,10
BHR
Bahrain
133
57,14
65,17
BGD
Bangladesh
17
67,53
33,11
BRB
Barbados
148
19,48
73,10
BLR
Belarus
44
53,25
62,33
BEL
Belgium
81
18,18
37,10
BEN
Benin
80
58,44
25,83
BOL
Bolivia
79
31,17
42,09
BIH
Bosnia and Herzegovina
126
28,57
49,31
DWA
Botswana
144
29,87
41,96
BRA
Brazil
9
51,95
59,11
BRN
Brunei
153
41,56
67,50
BGR
Bulgaria
58
74,03
62,06
CHM
Cambodia
30
23,38
34,59
CMR
Cameroon
42
32,47
28,28
AFG
ALB
DZA
ATG
ARG
ARM
AUS
AUT
AZE
BHS
BHR
BGD
BRB
BLR
BEL
BEN
BOL BIH
BRA
BRN
BGR
CMR
CAN
CHL
CHN
COL
COD
CIV
HRV
CUB
CYP
CZE
DNK
EGY
DMA
SLV
ETH
GNQ
EST
FJI
FIN
FRA
GEO
GRC
GRD
GTM
GNB
GUY
HTI
HND
HUN
IND IDN
IRN
IRL
ISR
ITA
JAM
JPN
JOR
KAZ
KEN
KGZ
LVA
LBY
LTU
LUX
MDG
MWI
MYS
MLI
MLT
MUS
MEX
MDA
MNG
MNE
MAR
MOZ MMR
NAM
NPL
NLD
NIC
N…
MMC
NOR
OMN
PAK
PAN
PRY PER
PHL
POL PRT
QAT
ROU
RUS
RWA
SAU
SEN
SRB
SYC
SGP
SVK
SVN
ZAF
ESP
LKA
SUR
SWE
CHE
SAR
TJK
TZA
THA
TTO
TUN
TUR
UGA
UKR
UAE
GBR
USA
URY
UZB
VEN
VNM
YEM
ZMB
ZWE
0
10
20
30
40
50
60
70
80
90
100
15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
NCSI
DDL
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
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8 VOLUME XX, 2024
CAN
Canada
19
70,13
75,96
CHL
Chile
68
59,74
61,44
CHN
China
11
51,95
62,41
COL
Colombia
32
53,25
52,08
COD
Congo, Dem. Rep.
130
5,19
18,91
CIV
Cote d'Ivoire
65
44,16
33,54
Croatia
102
83,12
64,63
HRV
Cuba
136
16,88
29,10
CUB
Cyprus
123
66,23
68,83
CYP
Czech Republic
62
90,91
69,21
CZE
Denmark
112
84,42
82,68
DNK
Dominican Republic
71
71,43
45,21
DO
Ecuador
43
53,25
45,57
EC
Egypt
35
57,14
46,93
EGY
El Salvador
95
24,68
39,17
SLV
Estonia
107
93,51
75,59
EST
Ethiopia
36
32,47
20,70
ETH
Fiji
152
14,29
44,90
FJ
Finland
99
85,71
78,35
FIN
France
23
84,42
77,29
FRA
Georgia
52
64,94
53,50
GEO
Germany
26
90,91
80,01
GRC
Ghana
29
46,75
40,68
GRD
Greece
76
89,61
64,02
GTM
Guatemala
104
24,68
35,43
GNB
Guyana
155
10,39
42,91
GUY
Haiti
87
7,79
26,46
HTI
Honduras
105
22,08
35,09
HND
Hungary
73
67,53
64,25
HUN
India
1
67,53
40,02
IND
Indonesia
7
63,64
47,41
IRN
Iran
28
19,48
51,04
IRL
Ireland
115
75,32
75,18
ISR
Israel
75
67,53
75,70
ITA
Italy
37
79,22
67,26
JAM
Jamaica
93
41,56
48,18
JPN
Japan
18
63,64
78,69
JOR
Jordan
66
26,57
54,07
KAZ
Kazakhstan
46
48,05
60,18
KEN
Kenya
21
41,56
37,14
KGZ
Kyrgyzstan
72
37,66
42,96
LVA
Latvia
100
75,32
66,23
LBY
Libya
119
10,39
41,10
LTU
Lithuania
88
93,51
67,34
LUX
Luxembourg
128
66,23
78,40
MDG
Madagascar
125
14,29
22,80
MWI
Malawi
92
27,27
23,20
MYS
Malaysia
38
79,22
62,19
MLI
Mali
124
19,48
26,00
MLT
Malta
139
50,65
71,74
MUS
Mauritius
117
44,16
53,57
MEX
Mexico
16
37,66
51,46
MDA
Moldova
60
57,14
56,79
MNG
Mongolia
86
18,18
46,41
MNE
Montenegro
132
35,06
57,79
MAR
Morocco
20
70,13
46,88
MOZ
Mozambique
82
9,09
24,88
MMR
Myanmar
53
10,39
34,29
NAM
Namibia
140
28,57
37,28
NPL
Nepal
56
28,57
30,58
NLD
Netherlands
39
83,22
81,86
NIC
Nicaragua
94
29,87
32,70
NIG
Nigeria
2
54,55
31,76
MMC
North Macedonia
113
58,44
55,36
NOR
Norway
111
67,53
80,19
OMN
Oman
137
45,45
59,51
PAK
Pakistan
8
41,56
32,23
PAN
Panama
97
50,65
48,43
PRY
Paraguay
96
63,64
42,58
PER
Peru
49
62,34
48,23
PHL
Philippines
6
63,64
45,99
POL
Poland
34
87,01
65,03
PRT
Portugal
59
89,61
68,46
QAT
Qatar
138
58,44
64,99
ROU
Romania
61
89,61
59,84
RUS
Russia
13
71,43
65,12
RWA
Rwanda
142
33,77
30,23
SAU
Saudi Arabia
57
84,42
63,89
SEN
Senegal
83
19,48
33,04
SRB
Serbia
67
80,52
59,81
SYC
Seychelles
143
11,69
50,30
SGP
Singapore
77
71,43
79,93
SVK
Slovakia
98
83,12
65,44
SVN
Slovenia
101
67,53
69,74
ZAF
South Africa
31
36,36
49,24
ESP
Spain
22
88,31
72,21
LKA
Sri Lanka
50
44,16
43,02
SUR
Suriname
149
22,08
51,50
SWE
Sweden
70
84,42
81,51
CHE
Switzerland
63
75,32
82,93
SAR
Syria
131
15,58
33,40
TJK
Tajikistan
135
10,39
34,56
TZA
Tanzania
24
24,68
26,96
THA
Thailand
10
64,94
56,63
TTO
Trinidad and Tobago
129
33,77
52,60
TUN
Tunisia
55
53,25
46,26
TUR
Turkey
12
61,04
58,29
UGA
Uganda
45
50,65
26,71
UKR
Ukraine
5
75,32
55,96
UAE
United Arab Emirates
78
40,26
68,87
GBR
United Kingdom
14
89,61
79,96
USA
United States
4
64,94
81,05
URY
Uruguay
121
59,74
63,86
UZB
Uzbekistan
25
36,36
49,00
VEN
Venezuela
40
28,57
43,14
VNM
Vietnam
3
36,36
47,69
YEM
Yemen
54
7,79
18,00
ZMB
Zambia
120
55,84
29,66
ZWE
Zimbabwe
103
15,58
28,97
Therefore, the need to use specific methods to identify latent
relationships between these indicators is justified.
Notes. The higher the level of cryptocurrency use in a country,
the higher the GCAI rating and the smaller the bubble size.
The smallest bubble size corresponds to the country with the
highest GCAI rating.
D. DATA SET AND PRELIMINARY DATA PREPARATION
FOR CORRESPONDENCE ANALYSIS
The issue of studying the main risks of using cryptocurrencies
is important for the personal and financial security of
individual users, businesses, governance, and cybersecurity of
countries around the world. Therefore, it is important to study
various aspects related to the risk factors of the cryptocurrency
environment.
To establish the implicit relationships between the individual
dimensions of the cryptocurrency environment discussed in
Sections 3.1-3.3, which are not obvious due to the specific
methodology of their calculation. In particular, in the GCAI,
countries with the same Index score have different Index ranks
[32]. These features require additional research.
To establish the implicit relationships between fraud
cryptocurrency crime totals, NCSI, GCAI, DDL, and GDP
[32, 45], a correspondence analysis was conducted between
the following pairs of indicators: fraud cryptocurrency crime
totals, and GDP, fraud cryptocurrency crime totals and DDL;
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
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O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
VOLUME XX, 2024 9
fraud cryptocurrency crime totals and GCAI; fraud
cryptocurrency crime totals and NCSI; NCSI and GCAI.
Correspondence analysis is an exploratory analysis that allows
you to build a visual association model between categories of
two nominal variables. The correspondence table of the
variables under study is an image in a multidimensional space.
Its rows and columns represent points and calculate the
distances between them. The goal of correspondence analysis
is to find the space of the lowest dimensionality in which the
data points representing the categories of the original variables
can be reproduced with maximum accuracy [47, 48]. Such
unique insights can only be obtained through correspondence
analysis.
The values of the National Cyber Security Index (ranging from
1.30 to 94.81), Digital Development Level (ranging from
11.30 to 82. 93), GDP (in the range of 241 and 22939581 $
million), fraud cryptocurrency assets crime totals (in the
range of 4 and 2269521 $ thousands) and Global Crypto
Adoption Index score (in the range of 0 to 1), which are
unevenly distributed among countries, were divided into
groups (low, medium, high) according to the following rule
(Table III):
TABLE III
TABLE OF THE DISTRIBUTION OF COUNTRIES IN THE WORLD INTO GROUPS
BY NCSI, GCAI, FRAUD CRYPTOCURRENCY CRIME TOTALS, DDL, AND
GDP.
NCSI
GCAI
Fraud
Cryptocurrency
Crime Totals
DDL
GDP
Low
< 30
< 0.001
<1,000
< 30
<10,000
Medium
30‒60
0.001‒0.010
1,000‒100,000
30‒60
10,000‒
1,000,000
High
>= 60
>= 0.010
>= 100,000
>= 60
>= 1,000,000
To evaluate the quality of the representation of the original
data as points in an n-dimensional space, the correspondence
analysis uses statistics 2. The value of this criterion is a
measure of proximity (distance) between points in space. To
display the relationships between the categories of the studied
variables, it is necessary to find the space of the smallest
dimension in which the distances between the points
representing the input data will be minimally distorted. In this
case, the points of the n-dimensional space will reproduce the
relationships between the features (categories of variables) as
accurately as possible. The smaller the calculated distance
between the points, the greater the similarity between the
categories, and vice versa. The goal of correspondence
analysis is to identify the underlying dimensions that explain
the pattern of correspondence between two categorical
variables.
IV. RESULTS
A. CORRESPONDENCE ANALYSIS MODELS FOR
EVALUATIONS OF THE CRYPTOCURRENCY CRIME
RISKS
Table IV-VIII presents the calculated eigenvalues that
determine the number of space dimensions sufficient for a
high-quality visual representation of the input data categories
for each of the analyzed pairs of variables.
TABLE IV
EIGENVALUES AND INERTIA FOR ALL DIMENSIONS OF FRAUD
CRYPTOCURRENCY CRIME TOTALS AND GDP
Number
of Dims.
Total Inertia=2.0000
Singular
Values
Eigen-
Values
Perc. of Inertia
Cumulative
Percent
Chi Squares
1
0.946796
0.896423
44.82116
44.8212
94.33339
2
0.723578
0.523566
26.17828
70.9994
55.09642
3
0.690242
0.476434
23.82172
94.8212
50.13667
4
0.321833
0.103577
5.17884
100.0000
10.89970
For a quality representation of the highlighted categories of
variables, fraud cryptocurrency crime totals, and GDP, four
dimensions are needed. The first dimension in this case
“extracts” 44.82% of the total inertia, the second ‒ 26.18%,
the third ‒ 23.82%, and the inclusion of the fourth dimension
increases the "extracted" inertia to 100% (Table 2, Figure 5).
An objective assessment of the proximity of empirical and
theoretical distributions in the analysis of correspondence is
the χ²-test. In our case, the significance level p-level < 0.01.
Therefore, the obtained results of the correspondence analysis
are statistically significant. The number of degrees of freedom
df = 25, χ2e = 210.47. χ20(0.01;25) = 44.3. χ2e > χ20. This means
that the expected values are sufficiently close to the observed
ones.
From the 3D Plot of Column Coordinates (Figure 5), we can
conclude that there are correlations between a low level of
Fraud total and a medium level of GDP, between a high level
of Fraud total and a low level of GDP, and between the
medium level of Fraud total and medium level of GDP for the
countries under study. Thus, there is no obvious connection
between the level of economic development of a country and
the level of cryptocurrency fraud.
FIGURE 5. The 3D plot of column coordinates of fraud cryptocurrency
crime totals and GDP.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
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10 VOLUME XX, 2024
For a quality representation of the crosstab table values of the
variables fraud c cryptocurrency crime totals and DDL, four
dimensions are needed. The first dimension, in this case,
"extracts" 33.59% of the total inertia, the second - 30.33%, and
the third – 19.67%, the inclusion of the fourth dimension
increases the "extracted" inertia to 100% (Table V, Figure 6),
p-level < 0.01, df = 25, 𝑎2+ 𝑏2= 𝑐2χ2e = 138.46 χ20(0.01;25)
= 44.3. χ2e > χ20.
FIGURE 6. The 3D plot of column coordinates of fraud cryptocurrency
crime totals and DDL.
Figure 6 suggests that there is a correlation between a low level
of Fraud total and a low level of DDL, between a medium level
of Fraud total and a medium level of DDL, and between a high
level of Fraud total and a high level of DDL for the countries
under study. Thus, one can assume that a correlation exists
between the digital development level of a country and fraud
cryptocurrency crime totals.
TABLE V
EIGENVALUES AND INERTIA FOR ALL DIMENSIONS OF FRAUD
CRYPTOCURRENCY CRIME TOTALS AND DDL
Number
of Dims.
Total Inertia=2.0000
Singular
Values
Eigen-
Values
Perc. of Inertia
Cumulative
Percent
Chi Squares
1
0.819602
0.671748
33.58740
33.5874
46.50619
2
0.778878
0.606651
30.33254
63.9199
41.99939
3
0.627176
0.393349
19.66746
83.5874
27.23219
4
0.572933
0.328252
16.41260
100.0000
22.72540
Four dimensions are required to display the majority of
information about the differences between rows representing
cross-table values of the variables fraud cryptocurrency crime
totals and GCAI. The first dimension, in this case, "extracts"
38.87% of the total inertia, the second – 34.24%, and the third
– 15.76%, the inclusion of the fourth dimension increases the
"extracted" inertia to 100% (Table VI, Figure 7). p-level <
0.01, df = 25, χ2e = 146.65. χ20 (0.01;25) = 44.3. χ2e > χ20.
FIGURE 7. The 3D plot of column coordinates of fraud cryptocurrency
crime totals and GCAI.
TABLE VI
EIGENVALUES AND INERTIA FOR ALL DIMENSIONS OF FRAUD
CRYPTOCURRENCY CRIME TOTALS AND GCA
Number
of Dims.
Total Inertia=2.0000
Singular
Values
Eigen-
Values
Perc. of Inertia
Cumulative
Percent
Chi Squares
1
0.881663
0.777330
38.86651
38.8665
56.99882
2
0.827510
0. 684773
34.23866
73.1052
50.21195
3
0.561450
0.315227
15.76134
88.8665
23.11444
4
0.471879
0.222670
11.13349
100.0000
16.32757
Four dimensions are needed to qualitatively represent the
initial values of the variables fraud cryptocurrency crime
totals and NCSI. The first dimension, in this case, represents
almost 32% of the total inertia; the inclusion of the second
increases the "extracted" inertia to 60%, the inclusion of the
third to almost 82%, and the fourth to 100% (Table VII, Figure
8). p-level < 0.01, df = 25, χ2e= 138.25. χ20(0.01;25) = 44.3.
χ2e> χ20.
TABLE VII
EIGENVALUES AND INERTIA FOR ALL DIMENSIONS OF FRAUD
CRYPTOCURRENCY CRIME TOTALS AND NCSI
Number
of Dims.
Total Inertia=2.0000
Singular
Values
Eigen-
Values
Perc. of Inertia
Cumulative
Percent
Chi Squares
1
0.798079
0.636931
31.84653
31.8465
44.02669
2
0.755034
0.570076
28.50382
60.3504
39.40552
3
0.655686
0.429924
21.49618
81.8465
29.71769
4
0.602552
0.363069
18.15347
100.0000
25.09652
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
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O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
VOLUME XX, 2024 11
FIGURE 8. The 3D plot of column coordinates of fraud cryptocurrency
crime totals and NSCI.
From the 3D Plot of Column Coordinates (Figure 8), it can be
concluded that there are correlations between a high level of
Fraud total and a low level of NCSI, between a medium level
of Fraud total and a high level of NCSI, and between a low
level of Fraud total and a medium level of NCSI for the
investigated countries. Therefore, the preliminary visual
analysis does not provide grounds to assert the existence of a
correlation between the level of cybersecurity and the level of
fraud cryptocurrency crime totals in a specific country.
For a quality representation of the categories of variables
NCSI and GCAI, four dimensions are needed. The first
dimension in this case “extracts” 35.30% of the total inertia,
the second - 26.30%, the third - 27.70%, and the inclusion of
the fourth dimension increases the “extracted” inertia to 100%
(Table VIII, Figure 9). The significance level is p-level < 0.01.
The number of degrees of freedom is df = 25, χ2e = 612.3.
χ20(0.01;25) = 44.3. χ2e> χ20.
TABLE VIII
EIGENVALUES AND INERTIA FOR ALL DIMENSIONS OF FRAUD
CRYPTOCURRENCY CRIME TOTALS AND NCSI
Number
of Dims.
Total Inertia=2.0000
Singular
Values
Eigen-
Values
Perc. of Inertia
Cumulative
Percent
Chi Squares
1
0.840227
0.705982
35.29909
35.2991
216.2405
2
0.724941
0.525539
26.27696
61.5761
160.9714
3
0.688811
0.474461
23.72304
85.2991
145.3262
4
0.542234
0.294018
14.70091
100.0000
90.0570
The 3D Plot of Column Coordinates for NCSI and GCAI
(Figure 9) suggests that there is a correlation between low
NCSI and high GCAI, between medium NCSI and medium
GCAI, and between high GCAI and low DDL for the countries
studied. Thus, one can assume that the level of mass adoption
of cryptocurrency inversely correlates with the country's cyber
defense.
FIGURE 9. 3D plot of column coordinates of GCAI and NCSI.
The significance level is p-level < 0.01, and χ2e> χ20 for all
pairs of variables. Thus, the results of the correspondence
analysis are statistically significant, and the expected values
are quite close to the observed ones.
B. ASSESSMENT OF SOLUTION QUALITY
We calculate special statistics to assess the quality of the visual
representation of points representing input data in n-
dimensional space. It is necessary that all or most of the point-
rows (point-columns) are accurately represented, with
undistorted distances between them. Tables IX‒XIII show the
calculated statistics for the corresponding row coordinates.
TABLE IX
COLUMN COORDINATES AND CONTRIBUTIONS TO INERTIA FOR FRAUD
CRYPTOCURRENCY CRIME TOTALS AND GDP
Row
Name
Row
Num.
Coordinate Dimension
Mas
Relative
Inertia
1
2
3
4
Fraud
totals:
high
1
1.22927
0.51635
-0.35033
0.65793
0.15000
0.17500
Fraud
totals:
medium
2
-0.15404
-1.00312
0.68060
-0.08245
0.20000
0.15000
Fraud
totals:
low
3
-1.02389
0.82115
-0.55713
-0.54800
0.15000
0.17500
GCAI:
high
4
0.88896
1.08948
0.73920
-0.47578
0.13333
0.18333
GCAI:
medium
5
0.42292
-0.93252
-0.63270
-0.22635
0.20000
0.15000
GCAI:
low
6
-1.21867
0.24744
0.16788
0.65225
0.16667
0.16667
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

12 VOLUME XX, 2024
TABLE X
COLUMN COORDINATES AND CONTRIBUTIONS TO INERTIA FOR FRAUD
CRYPTOCURRENCY CRIME TOTALS AND DLL
Row
Name
Row
Num.
Coordinate Dimension
Mas
Relative
Inertia
1
2
3
4
Fraud
totals:
high
1
-0.03771
-1.34858
1.08592
0.02634
0.12600
0.18750
Fraud
totals:
medium
2
0.81214
0.42934
-0.34515
-0.56771
0.21875
0.14063
Fraud
totals:
low
3
-1.10683
0.47779
-0.38473
0.77371
0.15625
0.17186
GCAI:
high
4
0.15397
-0.46473
-0.37422
0.10763
0.36938
0.07031
GCAI:
medium
5
-1.18144
0.95188
0.76648
1.65275
0.03125
0.23438
GCAI:
low
6
2.36432
2.01282
1.62078
-0.623137
0.10000
0.30000
TABLE XI
COLUMN COORDINATES AND CONTRIBUTIONS TO INERTIA FOR FRAUD
CRYPTOCURRENCY CRIME TOTALS AND GCAI
Row
Name
Row
Num.
Coordinate Dimension
Mas
Relative
Inertia
1
2
3
4
Fraud
totals:
high
1
1.22927
0.51635
-0.35033
0.65793
0.15000
0.17500
Fraud
totals:
medium
2
-0.15404
-1.00312
0.68060
-0.08245
0.20000
0.15000
Fraud
totals:
low
3
-1.02389
0.82115
-0.55713
-0.54800
0.15000
0.17500
GCAI:
high
4
0.88896
1.08948
0.73920
-0.47578
0.13333
0.18333
GCAI:
medium
5
0.42292
-0.93252
-0.63270
-0.22635
0.20000
0.15000
GCAI:
low
6
-1.21867
0.24744
0.16788
0.65225
0.16667
0.16667
TABLE XII
COLUMN COORDINATES AND CONTRIBUTIONS TO INERTIA FOR FRAUD
CRYPTOCURRENCY CRIME TOTALS AND NSCI
Row
Name
Row
Num.
Coordinate Dimension
Mas
Relative
Inertia
1
2
3
4
Fraud
totals:
high
1
-1.36984
-0.35557
-0.30878
1.00668
0.12121
0.18939
Fraud
totals:
medium
2
0.63459
0.56891
-0.49405
-0.47912
0.22727
0.13636
Fraud
totals:
low
3
0.13598
1.13782
0.98810
-0.10267
0.15152
0.17424
GCAI:
high
4
-0.49654
-0.25370
0.22032
-0.37489
0.33333
0.08333
GCAI:
medium
5
0.49654
1.37723
-1.19601
0.37490
0.10606
0.19697
GCAI:
low
6
1.86203
-1.01480
0.88127
1.40584
0. 06061
0.21970
TABLE XIII
COLUMN COORDINATES AND CONTRIBUTIONS TO INERTIA FOR FRAUD
CRYPTOCURRENCY CRIME TOTALS AND NSCI
Row
Name
Row
Num.
Coordinate Dimension
Mas
Relative
Inertia
1
2
3
4
Fraud
totals:
high
1
-1.17618
0.29886
0.25097
0.75855
0.16071
0.22340
Fraud
totals:
medium
2
0.28599
-1.02767
0.86299
-0.18445
0.17143
0.14185
Fraud
totals: low
3
0.83406
0.76340
-0.64106
-0.53791
0.16786
0.13475
GCAI:
high
4
0.86862
-0.57978
-0.48687
0.56020
0.18929
0.15603
GCAI:
medium
5
0.05200
1.07619
0.90373
0.03354
0.16786
0.16667
GCAI:
low
6
-1.21202
-0.49631
-0.41677
-0.78166
0.14286
0.17731
In 4-dimensional space, the quality of the representation of
row (column) points for all analyzed pairs of variables is
maximum: Quality = 1, which makes it possible to represent
the categories of the initial variables and the relation-ships
between them as accurately as possible.
The Mass column contains information about the distribution
of mass (fraction) by table cells or by points in space. The
Relative Inertia column contains the calculated value of the
share of the total inertia of the corresponding point, which
does not depend on the dimensionality of the space (the quality
of the point representation can be high, but the share of this
point in the total inertia may be insignificant). In our study, the
calculated quality of representation of each point does not
differ significantly from the value of its contribution to the
total inertia.
C. GRAPHICAL ANALYSIS OF CORRESPONDENCE
ANALYSIS RESULTS
The graphical analysis of the correspondence analysis results
makes the most sense. The 2D plot of column coordinates
(Figures 10‒14) shows the percentage of total inertia
explained by the corresponding eigenvalue. The horizontal
axis in the 2D plane corresponds to the maximum inertia. The
intersection of the vertical and horizontal coordinate axes is
the center of gravity of the observed points. The smaller the
distance between points representing categories of the same
variable, the stronger the relationship between these
categories. To evaluate the relationship between points
representing categories of different variables, the angles
formed by the lines connecting these points with the center of
gravity - the point with coordinates (0;0) are determined. If the
angle is acute, the corresponding categories of variables are
positively correlated. If the angle is obtuse, the correlation
between the categories of variables is inverse. A right angle
indicates no relationship. The scatterplots show a global
pattern within the data.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
VOLUME XX, 2024 13
FIGURE 10. The 3D plot of column coordinates of fraud cryptocurrency
crime totals and GDP.
FIGURE 11. The 2D plot of column coordinates of fraud cryptocurrency
crime totals and DDL.
FIGURE 12. The 2D plot of column coordinates of fraud cryptocurrency
crime totals and NCSI.
FIGURE 13. The 2D plot of column coordinates of fraud cryptocurrency
crime totals and GCAI.
FIGURE 14. The 2D plot of column coordinates of NCSI and GCAI.
V. DISCUSSION
The modern cryptocurrency environment is complex,
multifaceted, and changing. Cryptocurrencies are rapidly
penetrating the most important areas of society and are having
a significant impact on the welfare of citizens, the financial
industry, e-commerce, governance, and cybersecurity of the
cryptocurrency environment do not always occur in wealthy
countries with advanced IT. Cryptocurrencies have no
geographical boundaries and leave no digital traces. The
cryptocurrency environment attracts cybercriminals, which
results in a growing number of cryptocurrency crimes in the
world. However, not all countries have the same level of
cryptocurrency crime. Therefore, governments need to
understand the significant factors that affect both the
development of the cryptocurrency world and national
security, particularly its economic and cybersecurity
dimensions.
This study analyzed the main factors that may be associated
with cryptocurrency crime: GDP, digital development (DDL),
e-commerce market size, mass adoption of cryptocurrency
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

14 VOLUME XX, 2024
(GCAI), cybersecurity (NCSI), and cryptocurrency crime for
selected countries. The analysis becomes complicated because
different rankings do not include all countries. In addition,
most of the relationships between cryptocurrency environment
factors are not obvious due to the anonymity of cryptocurrency
transactions and imperfect estimates of cryptocurrency
environment determinants. For example, fraud cryptocurrency
crime totals are updated yearly after considering
cryptocurrency transactions on the Deep Web. Some of the
cryptocurrency environment indicators are not available to the
general public. Therefore, such studies require the use of
specific analysis methods.
To identify the implicit relationships between fraud
cryptocurrency crime totals, NCSI, GCAI, DDL, and GDP
[32, 44], we conducted a correspondence analysis between the
following pairs of indicators: fraud cryptocurrency crime
totals and GDP; fraud cryptocurrency crime totals and DDL;
fraud cryptocurrency crime totals and GCAI; fraud
cryptocurrency crime totals and NCSI; NCSI and GCAI.
We found that the level of GDP and the level of fraud
cryptocurrency crime show no correlation (Figure 10). This
result can be explained by the fact that cryptocurrency crimes
are committed digitally and online. As long as there is internet
access, these crimes can occur from anywhere, regardless of a
country's economic development or GDP level. Even lower
GDP nations may have sufficient technological infrastructure
for crypto crimes. Crypto fraudsters can target victims
globally, making a country's own GDP less relevant.
Motivations behind crypto fraud like financial gain, ideology
or disruption may be independent of a nation's GDP status.
Both wealthy and poorer countries can have actors driven by
similar incentives. There may also be underreporting or lack
of data on crypto crimes, especially from developing nations,
which can skew analyses against GDP data. Additionally,
cryptocurrency regulations vary significantly across countries
regardless of GDP levels, potentially allowing crimes to thrive
in unexpected locations. Crypto users are globally dispersed,
not necessarily concentrated in high or low-GDP nations.
R. Miśkiewicz et al. analyzed the relationship between crypto
trading and a country's economic development. The
researchers concluded that an increase in cryptocurrency
trading led to growth in GDP, real gross fixed capital
formation, and globalization. However, in the long-term
perspective, empirical results did not confirm the link between
cryptocurrency trading and economic development growth
[49].
Also exists a fairly strong positive correlation between fraud
cryptocurrency crime and digital development (Figure 11) and
the level of mass adoption of cryptocurrency (Figure 12).
The foundational technological infrastructure underpinning
cryptocurrencies and blockchain can be complex, and not
everyone fully understands the associated risks and
vulnerabilities. This knowledge gap can be exploited by bad
actors to perpetrate fraud or engage in other illegal activities.
That correlation does not necessarily imply causation, and
there could be other factors contributing to this relationship.
Additionally, the correlation may vary in strength depending
on the specific regions, periods, or contexts being analyzed.
However, the research literature does not cover the results of
research on these issues. They require further careful analysis.
We confirmed a fairly strong negative correlation between the
level of fraud cryptocurrency crime and the level of cyber
defense of a particular country (Figure 13). There is also a
fairly strong positive correlation between the level of mass
adoption of cryptocurrency and the level of cybersecurity
(Figure 14).
Bolstering cyber defense capabilities can curtail the avenues
for fraud and crimes involving cryptocurrencies.
Implementing robust cybersecurity measures, encompassing
encryption techniques, multi-factor authentication protocols,
and secure communication channels, erects formidable
barriers that hinder cybercriminals from exploiting
vulnerabilities and perpetrating fraudulent acts or illicit
activities related to cryptocurrencies. Our conclusions
confirmed the results of research by L. Albshaier et al., which
established a connection between hacker attacks in e-
commerce and the level of cybercrime [50]. The authors R.
Apau et al. confirmed the relationship between the use of e-
commerce technologies and the growth of cybercrime. They
emphasized the lack of research in this field, especially in
developing countries [51].
Thus, a high level of digital development, a significant spread
of e-commerce market size, and significant mass adoption of
cryptocurrencies cause significant cryptocurrency fraud and
are risk factors for developing cryptocurrency in a particular
country. In turn, cybercrime is a threat to national cyber
defense. However, the high economic development of a
country is not an indication of the spread of cryptocurrency.
Our research is not without limitations. First of all, due to the
lack of consolidated and reliable data on the mutual influence
of cryptocurrencies and cybercrime. In addition, some of the
indices used in our case studies are approximate estimates that
are dynamically updated. Also, not all indicators of the
cryptocurrency environment are available to the general
public. Nevertheless, the proposed visual models can help to
better understand the potential impact of the analyzed
determinants of cryptocurrency on the formation of a secure
cryptocurrency environment in certain countries.
VI. CONCLUSIONS
This study aims to investigate the impact of basic indicators
of the cryptocurrency environment, e-commerce, and e-
government on cryptocurrency crime risks and, in turn, on
the cybersecurity of countries worldwide. The identified
correlations between indicators associated with
cryptocurrency crime should provide decision-makers with
an understanding of the factors that pose a threat to the
personal financial security of citizens, the security of the
digital space, trust in e-commerce and e-government, the
cybersecurity of the country as a whole, and the need for
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
VOLUME XX, 2024 15
legal regulations for e-commerce and the cryptocurrency
market.
The main contribution of this study is to provide decision-
makers with a clear understanding of the basic factors of the
cryptocurrency environment that pose the greatest risk of
cryptocurrency crime based on the developed models and to
inform informed decision-making aimed at ensuring legal
regulations for the cryptocurrency market, enhancing the
security of the cryptocurrency environment and improving
cyber defense at the national level. Governments of
individual countries potentially using illegal cryptocurrency
markets to achieve their own goals pose significant threats to
global security. Therefore, the next step in our research
markets to achieve their own goals pose significant threats to
global security. Therefore, the next step in our research
ACKNOWLEDGMENT
The authors express their sincere gratitude to the Armed
Forces of Ukraine for providing security, which made it
possible to conduct our research.
APPENDIX A
GCAI, NCSI, and DDL of Countries in the World
See Table I.
APPENDIX B
GCAI, NCSI, and DDL of Countries in the World
See Table II.
ABBREVIATIONS
The following abbreviations are used in this manuscript:
IT ‒ Information Technologies
BNPL ‒ Buy Now Pay Later
B2B ‒ Business-to-Business
B2C ‒ Business-to-Consumer
C2C ‒ Consumer-to-Consumer
DDL ‒ Digital Development Level
DeFi ‒ Decentralized Finance
EGDI ‒ E-Government Development Index
GCAI ‒ Global Crypto Adoption Index
GDP ‒ Gross Domestic Product
NCSI ‒ National Cyber Security Index
NRI ‒ Networked Readiness Index
PPP ‒ Purchasing Power Parity
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OLHA KOVALCHUK received the M.S.
degree in applied mathematics from Ivan
Franko National University of Lviv, Lviv,
Ukraine, in 1993 and the Ph.D. degree in
mathematical modeling and computational
methods from the Institute of Cybernetics of
V.M. Glushkov NAS of Ukraine, Kyiv,
Ukraine, in 2006.
Since 2007 she has been working as an
associate professor and is currently position
Associate Professor at the Department of
Theory of Law and Constitutionalism at the
West Ukrainian National University,
Ternopil, Ukraine. This author is the author of more than 176 scientific
publications: articles, monographs, textbooks, and conference proceedings.
She is a reviewer for MDPI. Her research interests: data science, big data,
computing in mathematics, predictive policing, decision-making support in
justice, and information security.
RUSLAN SHEVCHUK received an M.S.
in computer systems and networks and a
Ph.D. degree in computer systems and
components from the Ternopil National
Economic University, Ukraine, in 2003
and 2008 respectively. He is currently
working as an Assistant Professor at the
Department of Computer Science and
Automatics of the University of Bielsko-
Biala, Poland, and an Assistant Professor at
the Department of Computer Science of the
West Ukrainian National University, Ukraine. His research interests include
information security, cryptographic transformations, and cyber-physical
systems. SERHIY BANAKH received an M.S.
degree in law from Ivan Franko National
University of Lviv, Lviv, Ukraine in 1997,
a Ph.D. degree in law from the Mariupol
State University, Mariupol, Ukraine, in
2015, and a Doctor of the Science of law
from the Zaporizhzhia National
University, Zaporizhzhia, Ukraine, in
2021.
Since 2014 he has been working as the
head of the legal department and dean of
the Law Faculty at the West Ukrainian
National University, Ternopil, Ukraine. He is the author of more than 124
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

O. Kovalchuk: Cryptocurrency Crime Risks Modeling: Environment, E-Commerce, and Cybersecurity Issue
17 VOLUME XX, 2024
scientific publications: articles, monographs, textbooks, and conference
proceedings. His research interests: predictive policing, internal security,
cybersecurity, and decision-making support in criminal justice. 02.2023‒
01.2024 ‒ Major of the Armed Forces of Ukraine. Awarded the Order of
Courage III degree, Golden Cross, Cross of Military Honor, and Medal for
the Defense of the Homelan.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3386428
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/