The cybersecurity modeling in critical infrastructures
The attempt to systematize modern approaches to mathematical modeling of cybersecurity of critical infrastructures was made as based on a survey of open publication argueing that modern modeling and simulation are unable to handle the complexity and inter-connectedness of such critical infrastructur...
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Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України
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nasplib_isofts_kiev_ua-123456789-1011422025-02-10T00:57:36Z The cybersecurity modeling in critical infrastructures Kravtsov, H. Kotsiuba, I. Prytulyuk, I. Применение методов и средств моделирования The attempt to systematize modern approaches to mathematical modeling of cybersecurity of critical infrastructures was made as based on a survey of open publication argueing that modern modeling and simulation are unable to handle the complexity and inter-connectedness of such critical infrastructures as smart grid. Предпринята попытка систематизировать современные подходы к математическому моделированию кибербезопасности критических инфраструктур на основе обзора открытых публикаций, в которых утверждается, что современное состояние математического и программного моделирования не позволяет охватить всю сложность и взаимосвязанность таких критических инфраструктур как, например, интеллектуальные сети в энергетике (смарт-грид). Зроблено спробу систематизації сучасних підходів до математичного моделювання кібербезпеки критичних інфраструктур на підставі огляду відкритих публікацій, в яких аргументується, що сучасний стан математичного та комп’ютерного моделювання (симуляції), парадигми управління не дозволяють охопити всю складність та взаємопов’язаність таких критичних інфраструктур як, наприклад, інтелектуальні мережі в енергетиці (смарт-грід). 2015 Article The cybersecurity modeling in critical infrastructures / H. Kravtsov, I. Kotsiuba, I. Prytulyuk // Электронное моделирование. — 2015. — Т. 37, № 4. — С. 75-84. — Бібліогр.: 19 назв. — англ. 0204-3572 https://nasplib.isofts.kiev.ua/handle/123456789/101142 004.056.5+620.9 en Электронное моделирование application/pdf Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України |
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Применение методов и средств моделирования Применение методов и средств моделирования |
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Применение методов и средств моделирования Применение методов и средств моделирования Kravtsov, H. Kotsiuba, I. Prytulyuk, I. The cybersecurity modeling in critical infrastructures Электронное моделирование |
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The attempt to systematize modern approaches to mathematical modeling of cybersecurity of critical infrastructures was made as based on a survey of open publication argueing that modern modeling and simulation are unable to handle the complexity and inter-connectedness of such critical infrastructures as smart grid. |
| format |
Article |
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Kravtsov, H. Kotsiuba, I. Prytulyuk, I. |
| author_facet |
Kravtsov, H. Kotsiuba, I. Prytulyuk, I. |
| author_sort |
Kravtsov, H. |
| title |
The cybersecurity modeling in critical infrastructures |
| title_short |
The cybersecurity modeling in critical infrastructures |
| title_full |
The cybersecurity modeling in critical infrastructures |
| title_fullStr |
The cybersecurity modeling in critical infrastructures |
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The cybersecurity modeling in critical infrastructures |
| title_sort |
cybersecurity modeling in critical infrastructures |
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Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України |
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2015 |
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Применение методов и средств моделирования |
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https://nasplib.isofts.kiev.ua/handle/123456789/101142 |
| citation_txt |
The cybersecurity modeling in critical infrastructures / H. Kravtsov, I. Kotsiuba, I. Prytulyuk // Электронное моделирование. — 2015. — Т. 37, № 4. — С. 75-84. — Бібліогр.: 19 назв. — англ. |
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Электронное моделирование |
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2025-12-02T08:38:34Z |
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UDC 004.056.5+620.9
H. Kravtsov, Cand. Sc. (technology)
I. Kotsiuba, I. Prytulyuk, post-graduate students
Pukhov Institute for Modeling in Energy Engineering of NAS of Ukraine
(15 General Naumov St, Kyiv, Ukraine, 03164,
(044) 4249165, email: hryhoriy.kravtsov@gmail.com, i.kotsiuba@gmail.com)
The cybersecurity modeling in critical infrastructures
The attempt to systematize modern approaches to mathematical modeling of cybersecurity of
critical infrastructures was made as based on a survey of open publication argueing that modern
modeling and simulation are unable to handle the complexity and inter-connectedness of such
critical infrastructures as smart grid.
Ïðåäïðèíÿòà ïîïûòêà ñèñòåìàòèçèðîâàòü ñîâðåìåííûå ïîäõîäû ê ìàòåìàòè÷åñêîìó ìîäå-
ëèðîâàíèþ êèáåðáåçîïàñíîñòè êðèòè÷åñêèõ èíôðàñòðóêòóð íà îñíîâå îáçîðà îòêðûòûõ ïóá-
ëèêàöèé, â êîòîðûõ óòâåðæäàåòñÿ, ÷òî ñîâðåìåííîå ñîñòîÿíèå ìàòåìàòè÷åñêîãî è ïðîãðàìì-
íîãî ìîäåëèðîâàíèÿ íå ïîçâîëÿåò îõâàòèòü âñþ ñëîæíîñòü è âçàèìîñâÿçàííîñòü òàêèõ êðèòè-
÷åñêèõ èíôðàñòðóêòóð êàê, íàïðèìåð, èíòåëëåêòóàëüíûå ñåòè â ýíåðãåòèêå (ñìàðò-ãðèä).
K e y w o r d s: critical infrastructure, cybersecurity, smart grid, vulnerability, mathematical
framework, mathematical model, agent-based simulation, game theory, algebraical approach.
Introduction. The national and financial security of country depends on reliable
functioning of critical infrastructure. Cybersecurity threats exploit the increased
complexity and connectivity of critical infrastructure systems, exposing the
country’s security, economy, and public safety and health to risk. Similar to fi-
nancial and reputational risk, cybersecurity risk affects a company’s bottom
line. It can drive up costs and impact revenue. It can harm organization’s ability
to innovate, gain and maintain customers [1].
Critical infrastructure is defined in the Executive Order 13636 (EO) “Im-
proving Critical Infrastructure Cybersecurity” (February 12, 2013, President
Obama) as “systems and assets, whether physical or virtual, so vital to the
United States that the incapacity or destruction of such systems and assets would
have a debilitating impact on security, national economics security, national
public health or safety, or any combination of those matters”. Due to the increas-
ing pressures from external threats, organizations responsible for critical infra-
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2015. Ò. 37. ¹ 4 75
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structure need to have a consistent and iterative approach to identifying, assess-
ing, and managing cybersecurity risk.
The critical infrastructure is a term used by governments to describe assets
that are essential for the society and economy functioning. Most commonly as-
sociated with the term are facilities for: (a) electricity generation, transmission
and distribution; (b) gas production, transport and distribution; (c) oil and
oil-products production, transport and distribution; (d) telecommunication; (e)
water supply (drinking water, waste water/sewage, stemming of surface water);
(f) agriculture, food production and distribution; (g) heating (e.g. natural gas,
fuel oil, district heating); (h) public health (hospitals); (i) transportation systems
(fuel supply, railway, airports); (j) financial services (banking, clearing); (k) se-
curity services (police, military).
The electricity generation, transmission and distribution are inalienable
parts of smart grid [2] that is of high priority for each economy in the world. We
will use smart grid as a basis for investigation of concerns for modeling the criti-
cal infrastructures because energy is a critical resource for everyone [3, 4].
Review of modeling problems in cybersecurity of the Smart Grid. Smart
grid integrates the traditional electrical power grid with information and com-
munication technologies (ICT). Such integration empowers the providers and
consumers of electrical utilities, improves the efficiency and availability of the
power system while constantly monitoring, controlling and managing the de-
mands of customers. A smart grid is a huge complex network composed of mil-
lions of devices and entities connected with each other (for example, 22 catego-
ries of interfaces between actors of the smart grid are defined) [5]. Such a mas-
sive network comes with many cybersecurity concerns. The author [6] defines
the cybersecurity of the smart grid as the urgent scientific problem. We will fol-
low the authors of [2, 5] in our understanding of vulnerabilities, attackers and
types of attacks.
The following vulnerabilities are the most serious in Smart grids:
1) Customer security: Smart meters autonomously collect massive amounts of
data and transport them to the utility company, consumer, and service providers.
This data includes private consumer information that might be used to infer the con-
sumer’s activities, devices being used, and times when the home is empty.
2) Greater number of intelligent devices: A smart grid has several intelligent
devices that are involved in managing both the electricity supply and network
demand. These intelligent devices may act as attack entry points within the net-
work. Moreover, the massiveness of smart grid network (100 to 1000 times
larger than the Internet) makes the network monitoring and management ex-
tremely difficult.
3) Physical security: Unlike the traditional power system, smart grid net-
work includes many components, and most of them are outside of the utility’s
H. Kravtsov, I. Kotsiuba, I. Prytulyuk
76 ISSN 0204–3572. Electronic Modeling. 2015. V. 37. ¹ 4
premises. This fact increases the number of insecure physical locations and
makes them vulnerable in case of physical access.
4) The lifetime of power systems: Since power systems coexist with the rela-
tively short lived IT systems, it happens that outdated equipment is still in ser-
vice. This equipment might act as weak security points and might not be thor-
oughly compatible with the current power system devices.
5) Implicit trust between traditional power devices: Device-to-device com-
munication in control systems is vulnerable to data spoofing where the state of
one device affects the actions of another. For instance, a device sending a false
state makes other devices behave unpredictably in an unwanted way.
6) Different Team’s backgrounds: Inefficient and unorganized communica-
tion between teams might cause a lot of improper decisions leading to rising vul-
nerability.
7) Use of Internet Protocol (IP) and commercial off-the-shelf hardware and
software: The use of IP standards in smart grids offers a big advantage as it pro-
vides compatibility between the various components. However, devices using IP
are inherently vulnerable to many IP-based network attacks such as IP spoofing,
Tear Drop, Denial of Service, and others.
8) More stakeholders: Availability of the huge number of stakeholders leads
to a very dangerous kind of attack: the insider attack.
Above mentioned vulnerabilities can be exploited by attackers with differ-
ent motives and expertise and could cause different levels of damage to the net-
work. Attackers could be script kiddies, elite hackers, terrorists, employees,
competitors or customers.
The attackers can be grouped into 5 groups:
1) Non-malicious attackers who view the security and operation of the sys-
tem as a puzzle to be cracked. Those attackers are normally driven by intellectual
challenge and curiosity.
2) Consumers driven by vengeance and vindictiveness towards other con-
sumers making them figure out ways to shut down their home’s power.
3) Terrorists who view the smart grid as an attractive target, since it affects
millions of people making the terrorists’ cause more visible.
4) Employees disgruntled on the utility/customers causes unintentional errors.
5) Competitors attacking each other for the sake of financial gain.
The major known attacks are:
1) Malware spreading: An attacker can develop malware and spread it to in-
fect smart meters or company servers. Malware can be used to replace or add any
function to a device or a system such as sending sensitive information.
2) Access through database links: Control systems record their activities in a
database on the control system network, then they mirror the logs into the busi-
The cybersecurity modeling in critical infrastructures
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2015. Ò. 37. ¹ 4 77
ness network. If the underneath database management systems are not properly
configured, a skilled attacker can gain access to the business network database,
and then use his skills to exploit the control system network.
3) Compromising communication equipment: An attacker may compromise
some of the communication equipment such as multiplexers causing a direct
damage or using it as a backdoor to launch future attacks.
4) Injecting false information (Replay Attack): An attacker can send packets
to inject false information in the network, such as wrong meter data, false prices,
fake emergency event, etc. Fake information can have huge financial impact on
the electricity markets.
5) Network Availability: Since smart grid uses IP protocol and TCP/IP
stack, it becomes subject to DoS attacks and to the vulnerabilities inherent in the
TCP/IP stack. DoS attacks might attempt to delay, block, or corrupt information
transmission in order to make smart grid resources unavailable.
6) Eavesdropping and traffic analysis: An adversary can obtain sensitive in-
formation by monitoring network traffic. Examples of monitored information
include future price information, control structure of the grid, and power usage.
7) Modbus security issue: The term SCADA refers to computer systems and
protocols that monitor and control industrial, infrastructure, or facility-based
processes such as smart grid processes. Modbus protocol is one piece of the
SCADA system that is responsible for exchanging SCADA information needed
to control industrial processes.
Given that the Modbus protocol was not designed for highly security-criti-
cal environments, several attacks are possible including:
sending fake broadcast messages to slave devices (Broadcast message
spoofing);
replaying genuine recorded messages back to the master (Baseline response
replay);
locking out a master and controlling one or more field devices (Direct slave
control);
sending benign messages to all possible addresses to collect devices’ infor-
mation (Modbus network scanning);
reading Modbus messages (Passive reconnaissance);
delaying response messages intended for the masters (Response delay);
attacking a computer with the appropriate adapters (Rouge interloper).
The described above vulnerabilities and attacks may be investigated by two
approaches as a result of: statistical observations on objects of real critical infra-
structures, and mathematical modeling (simulation) of such infrastructures.
Obviously, research in the area of cybersecurity for the objects of real criti-
cal infrastructures is unacceptable [7]. It defines a single possible way of con-
ducting researches — a mathematical modeling for scientific issues of cyber-
security in smart grid.
H. Kravtsov, I. Kotsiuba, I. Prytulyuk
78 ISSN 0204–3572. Electronic Modeling. 2015. V. 37. ¹ 4
Mathematical modeling and pessimism of Massoud Admin. Massoud
Admin [8] argues that modern modeling, simulation, and control paradigms are
unable to handle the complexity and inter-connectedness of such critical infra-
structures as smart grid. Let us enumerate the targets pertinent to the control
smart system:
1) Systems Science: How to retrofit and engineer a stable, secure, robust and
resilient grid with large numbers of such unpredictable power sources?
2) Modeling, Simulation, Control, and Optimization of Hybrid Systems
with Uncertainty: What roles will assets optimization, novel control algorithms,
increased efficiency, control/coordination of energy storage, advanced power
electronics, power quality, electrification of transportation, cyber security, poli-
cies, and technologies play in the smart grid of the future?
3) How can robust controls and observers be developed that can use secure
sensing to identify and build realistic models and appropriate responses? Will
they be able to adapt, control, and mitigate disturbances to achieve their goals?
4) Intelligent Transmission Systems/Assets include self-monitoring and
self-healing and the abilities to control real-time information for smart transmis-
sion utilization. Smart, cyber-physical secured control strategies need to be de-
veloped to handle congestions, precursors to instability or reliability problems.
Information exchange among renewable resources, and locational demands
from plug-in hybrids/fuel cell transport systems is to be incorporated.
5) Real time monitoring sensors, communication and control technologies
will constitute a linchpin infrastructure of smart power systems. However, the
increased use of electronic automation raises significant issues regarding the ade-
quacy of operational security of the automation and control systems. In addition,
the use of networked electronic systems for sending, metering, scheduling, trad-
ing, or e-commerce imposes numerous financial risks implied by the use of this
technology. A timely issue is merging of sensor-enabled data-based models with
derived models from first principles combined with online updating (also known
as data fusion in smart grid).
The Admin’s conclusion [8] is pessimistic: “Mathematical/theoretical foun-
dation is fragmented: Computational complexity, information theory, dynamical
systems and control science... need for a new science of interdependent complex
networks and infrastructure security”.
Mathematical modeling approaches. At the same time, many researchers
attempt to create mathematical framework, which can be able to solve the above
described concerns.
For example, Walid Saad together with his colleagues [9] proposes to im-
plement game theory for smart grid modeling. Their objectives are: to provide a
The cybersecurity modeling in critical infrastructures
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2015. Ò. 37. ¹ 4 79
comprehensive description of existing game theoretic applications in smart grid
networks, to identify key open problems in smart grid networks that are bound to
be addressed using game theory, and to pinpoint the main game theoretic tools
that can be adopted for designing the smart grid. Clearly, game-theoretic ap-
proaches present a promising tool for the analysis of smart grid systems. None-
theless, the advantages of applying distributed game-theoretic techniques in any
complex system such as the smart grid are accompanied by key technical chal-
lenges. First, one of the underlying assumptions in classical game-theoretic de-
signs is that the players are rational, i.e., each player makes his strategy choice so
as to optimize its individual utility and, thus, conform to some notion of equilib-
rium play. In practical control systems such as the smart grid, as the individual
system nodes interact and learn their strategies, one or more nodes might deviate
from the intended play and make non-rational decisions, i.e., choose unintended
strategies, due to various factors such a failure or delay in learning. These inac-
curate strategy choices can eventually lead to a non-convergence to the desired
equilibrium and, hence, impact the overall control system stability. The impact
of such bad decisions becomes more severe in practical deployments in which
the smallest perturbation of the system stability can lead to outages or other det-
rimental consequences. As a result, when designing game-theoretic models for
the smart grid, it is imperative to emphasize robustness in the model and algo-
rithm design.
Jurica Babic [10] sees the agent-based simulation as more appropriate ap-
proach for objectives of smart grid markets modeling. She makes a conclusion
that agent-based modeling is particularly suitable for the domain of energy busi-
ness since electricity markets can be defined as complex adaptive systems of in-
teractive agents. Cybersecurity and cybernetics are strongly related fields of hu-
man knowledge. This fact helps to understand that agent-based paradigm can be
successfully used for simulation of cybersecurity issues in smart grid as well.
Reinhard Laubenbacher and his co-authors [11] published a special over-
view of issues of agent-based simulation in 2012. They show the existing mathe-
matical frameworks (Cellular Automata, Hopfield Networks, Communicating
Finite State Machines, Interacting Particle Systems and Petri Nets) and propose
to use more relevant in their opinion approach of the finite dynamic system
(FDS).
They operate with the following terminology:
Agent-based simulation: an agent-based simulation of a complex system is a
computer model that consists of a collection of agents/variables that can take on
a typically finite collection of states. The state of an agent at a given point in time
is determined through a collection of rules that describe the agent’s interaction
H. Kravtsov, I. Kotsiuba, I. Prytulyuk
80 ISSN 0204–3572. Electronic Modeling. 2015. V. 37. ¹ 4
with other agents. These rules may be deterministic or stochastic. The agent’s
state depends on the agent previous state and the state collections of other agents
with whom it interacts.
Mathematical framework: a mathematical framework for agent-based simu-
lation consists of a collection of mathematical objects that are considered mathe-
matical abstractions of agent-based simulations. This collection of objects
should be general enough to capture the key features of most simulations, yet
specific enough to allow the development of a mathematical theory with mean-
ingful results and algorithms.
Finite dynamical system: a finite dynamical system is a time-discrete dy-
namical system on a finite state set. That is, it is a mapping from a Cartesian
product of finitely many copies of a finite set to itself. This finite set is often con-
sidered to be a field. Dynamics is generated by iteration of the mapping.
The approach proposed in overview [11] includes the descriptions of two
kinds of FDS: parallel update systems and sequential update systems. Unfortu-
nately, FDS has a set of disadvantages. The main disadvantage is impossibility
to define “deadlock” situation in parallel processes and difficulty for theoretical
evaluating of complexity of calculus that is fundamental aspect of time manage-
ment in high load systems. The results [11] do not permit describing in frame-
work such property of system elements as availability that is the major concern
for cybersecurity of smart grid.
Inability to take into account the structures of the transmitted data is addi-
tional disadvantage of FDS for objectives of cybersecurity simulation.
The term “process algebra” was coined in 1982 by Jan Bergstra and Jan Wil-
lem Klop [12], originally in the sense of universal algebra, to refer to a structure
satisfying a particular set of axioms [13]. Nowadays it is used in a more general
sense for algebraic approaches to describe and study concurrent processes.
The system behavior generally consists of processes and data. Processes are
the control mechanisms for the manipulation with data. While processes are dy-
namic and active, data is static and passive. The system behavior tends to be
composed of several processes that are executed concurrently, where these pro-
cesses exchange data in order to influence each other’s behavior. Fundamental
to process algebra is a parallel operator, to break down systems into their concur-
rent components. A set of equations is imposed to derive whether two terms are
behaviorally equivalent. In this framework, non-trivial properties of systems can
be established in a rigorous and elegant fashion. For example, it may be possible
to equate an implementation of a system to the specification of its required in-
put/output relation. A variety of automated tools have been developed to facili-
tate the derivation of such properties in a process algebraic framework. Abstract
The cybersecurity modeling in critical infrastructures
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2015. Ò. 37. ¹ 4 81
data types offer a framework in which the data can be also specified by means of
equations. �CRL [14] is a specification language, that combines process algebra
with equational specification of data types. Applications of the process algebra exist
in diverse fields such as safety-critical systems, network protocols, and biology.
�CRL allows investigating some problems of data fusion by the methods of
Category theory [15], where data fusion [16] is a “multilevel, multifaceted pro-
cess handling the automatic detection, association, correlation, estimation, and
combination of data and information from several sources”.
The process algebra in its different interpretations (Spy-calculus, �-calculus
and others) shows more appreciate result in the field of modeling cybersecurity
for distributed systems, but requires implementation of the different approaches
for evaluating specific properties of smart grid. For example, for evaluating per-
formance we should use the stochastic process algebra. The algebraic approach
based on process algebra and theory of category shows very good results in for-
mal analysis of security protocols for wireless sensor networks [18].
The correctness of different approaches usage for mathematical mode-
ling. The first part of the author’s research in the models of electronic digital sig-
nature functioning in smart grid was finished in 2014 [19]. The investigation of
relevant mathematical paradigms was one of the major issues. The following
known class of models was investigated and described: Protection systems that
verify and supervise themselves; Kuramoto oscillators; Percolation theory (ran-
dom fuse networks); evolutionary models; Markov processes; Neural networks.
The class of mathematical models of a non-stationary multidimensional ran-
dom field in the process of evolution was introduced by authors [19] as a more
relevant approach for evaluating the limit technical parameters during the mode-
ling of smart grid. It requires more accuracy to solve the tasks of minimization of
needs in labor and technical resources. But it may be seen as partial solution
only. It was the first step of immersion into issues of cybersecurity in smart grid.
The results obtained in the research allow us to see new horizons in the research
and development of more universal approach which will be devoid of the previ-
ously mentioned disadvantages.
Conclusion. We have shown that the modeling problem of cybersecurity in
the critical infrastructures is urgent. The algebraic approach may be a successful
way for solving the problem and can help in the design of the mathematical
framework of cyberspace modeling in critical infrastructures for safety evalua-
tion and risk assessment.
H. Kravtsov, I. Kotsiuba, I. Prytulyuk
82 ISSN 0204–3572. Electronic Modeling. 2015. V. 37. ¹ 4
Çðîáëåíî ñïðîáó ñèñòåìàòèçàö³¿ ñó÷àñíèõ ï³äõîä³â äî ìàòåìàòè÷íîãî ìîäåëþâàííÿ ê³áåðáåç-
ïåêè êðèòè÷íèõ ³íôðàñòðóêòóð íà ï³äñòàâ³ îãëÿäó â³äêðèòèõ ïóáë³êàö³é, â ÿêèõ àðãóìåí-
òóºòüñÿ, ùî ñó÷àñíèé ñòàí ìàòåìàòè÷íîãî òà êîìï’þòåðíîãî ìîäåëþâàííÿ (ñèìóëÿö³¿),
ïàðàäèãìè óïðàâë³ííÿ íå äîçâîëÿþòü îõîïèòè âñþ ñêëàäí³ñòü òà âçàºìîïîâ’ÿçàí³ñòü òàêèõ
êðèòè÷íèõ ³íôðàñòðóêòóð ÿê, íàïðèêëàä, ³íòåëåêòóàëüí³ ìåðåæ³ â åíåðãåòèö³ (ñìàðò-ãð³ä).
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Ïîñòóïèëà 02.06.15
ÊÐÀÂÖÎÂ Ãðèãîðèé Àëåêñååâè÷, êàíä. òåõí. íàóê, è.î. ñòàðøåãî íàó÷. ñîòð. Èí-òà ïðîáëåì
ìîäåëèðîâàíèÿ â ýíåðãåòèêå èì. Ã.Å. Ïóõîâà ÍÀÍ Óêðàèíû.  2000 ã. îêîí÷èë Ñåâàñòîïîëüñêèé
âîåííî-ìîðñêîé èíñòèòóò èì. Ï.Ñ. Íàõèìîâà. Îáëàñòü íàó÷íûõ èññëåäîâàíèé — êèáåðáåçî-
ïàñíîñòü ñìàðò-ãðèä, êðèïòîãðàôèÿ, ðàçðàáîòêà ðàñïðåäåëåííûõ ãåòåðîãåííûõ âû÷èñëè-
òåëüíûõ ñèñòåì.
ÊÎÖÞÁÀ Èãîðü Âàñèëüåâè÷, àñïèðàíò Èí-òà ïðîáëåì ìîäåëèðîâàíèÿ â ýíåðãåòèêå èì. Ã.Å. Ïó-
õîâà ÍÀÍ Óêðàèíû Äèðåêòîð ÎÎÎ «Èíòåãðåéòåä Ñîëþøíç».  2010 ã. îêîí÷èë Ãîñóäàðñò-
âåííûé óíèâåðñèòåò èíôîðìàöèîííî-êîìóíèêàöèîííûõ òåõíîëîãèé. Îáëàñòü íàó÷íûõ èññëå-
äîâàíèé — ïîñòåðèîðíûå ìåòîäû àíàëèçà óãðîç èíôîðìàöèîííîé áåçîïàñíîñòè.
ÏÐÈÒÓËÞÊ Èðèíà Àôàíàñüåâíà, àñïèðàíò Èí-òà ïðîáëåì ìîäåëèðîâàíèÿ â ýíåðãåòèêå
èì. Ã.Å. Ïóõîâà ÍÀÍ Óêðàèíû.  2011 ã. îêîí÷èëà Îòêðûòûé ìåæäóíàðîäíûé óíèâåðñèòåò
ðàçâèòèÿ ÷åëîâåêà «Óêðàèíà». Îáëàñòü íàó÷íûõ èññëåäîâàíèé — ìîäåëèðîâàíèå ôèíàíñîâûõ
ñèñòåì, òåîðèÿ èãð.
H. Kravtsov, I. Kotsiuba, I. Prytulyuk
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