-The article describes the modern Smart Grid from the standpoint of providing resistance to negative impacts, preventing them, and quickly restoring functions after accidents in accordance with the requirements of energy security. To implement this goal, the developed ontology of cyber-security of self-recovering Smart Grids has been proposed for implementation. A critical analysis of approaches and methods to ensure the sustainability of the functioning of power systems in the event of their destabilization. The ideology of sustainability of Smart Grid power systems on the basis of immunity was developed and a scheme for the formation of immunity for disturbances was proposed.
INTRODUCTION
Currently, in a number of developed countries, the technology of intelligent power grids Smart Grid (activeadaptive intelligent network) is widely used to deliver electricity to the consumer using modern digital technologies.
Smart Grid technologies, when implemented in the energy
systems, both existing and new, designed, can provide the
required innovative properties of the systems. Thanks to Smart
Grid energy saving is provided, costs are reduced, network
reliability and transparency of the management process are
increased. To implement large-scale programs for transforming
electric grids into intelligent ones and developing appropriate
standard solutions, the world's largest companies have
established the Smart Energy Alliance. It includes GE Energy
(General Electric), Capgemini, Cisco Systems, Siemens, HP,
Intel, SAP AG, Oracle, and others [
SUSTAINABILITY OF THE SMART GRID
Today, the most significant projects to create power grids based on the Smart Grid are carried out in the USA and Russia, in the countries of the European Union, as well as in Canada, Australia, China and Korea. In the last decade, various models have been developed to assess the readiness of electrical networks to convert to intelligent levels of Smart Grid technologies.
The first model was developed on the basis of the widely
used software industry maturity model (Maturity Model), led
by IBM, in 2007. As the utility industry embarks on the
transformation of the outdated power grid to the new smart
grid, it has to develop a shared vision for the smart grid
endstate and the path to its development and deployment. The
smart grid maturity model (SGMM) is presenting a consepsion
of the smart grid, the benefits it can bring and the various levels
of development. SGMM is helping numerous utilities
worldwide develop targets for their smart grid strategy, and
build roadmaps of the activities, investments and best practices
that will lead them to their future smart grid state. IBM worked
closely with members of the Intelligent Utility Network
Coalition (IUNC) to develop, discuss and revise several drafts
of the SGMM. Also, this team was assisted by APQC, a
member-based nonprofit organization that provides
benchmarking and best-practice research for approximately
500 organizations worldwide [
This model was brought to practical use by programmers from the Carnegie Mellon University, SEI (Software Engineering Institute). The Carnegie Mellon Software Engineering Institute was govern the SGMM model, working in conjunction with Carnegie Mellon University and the Carnegie Mellon Electricity Industry Center. Then, the institute was leverage its 20 years of experience with goal of workingout of the Capability Maturity Model Integration (CMMI).
In Russia, since 2011, a large-scale project to create an intelligent power system with an active-adaptive network (IPS AAN) is being implemented.
Expert working groups led by the Architectural Committee at the Scientific and Technical Council of JSC FGC UES and the Russian Academy of Sciences (RAS) developed the main provisions and approaches to the creation of a reference architecture of the said intellectual power system. As part of the implementation of this project in the UES of the East for the period until 2014 with the prospect of up to 2020, the IPS AAN polygon was created, which is a complex of software and hardware that form the environment for supporting the development of IPS AAN solutions.
The main purpose of the Polygon is to support the implementation of projects in the field of intellectual energy (Smart Grid) at all stages of the life cycle of these projects, as well as the implementation of a unique "ecosystem" that contributes to the sustainable innovation development of the power grid complex of the Russian Federation.
It is significant that in these projects the key is to make the future Smart Grid energy systems and the development of the following two new capabilities:
Resistance to negative impacts: the availability of special
methods for ensuring sustainability and survivability, reducing
the physical and information vulnerability of all components
of the energy system and contributing to both prevention and
rapid recovery from accidents in accordance with energy
security requirements [
Self-recovery in emergency situations: the power system and its elements should be able to maintain their technical condition continuously in an efficient state by identifying, analyzing and switching from management to the occurrence of a situation to a preventive (warning) occurrence. Selfrecovering power system should allow maximum possible to minimize disruptions (disturbances) with the help of an intelligent control system, including its most important component - the subsystem of cyber security.
Thus, an intelligent grid based on Smart Grid should be proactive in relation to changing operational conditions and monitor the impending technical problems before they can adversely affect its safety and the sustainability of the operation as a whole. Therefore, the components of the designed intellectual subsystems of cybersecurity should include the appropriate components of containment, prevention, detection, neutralization and self-recovery.
Multi-agent systems for coordinating control systems using a transient regimes monitoring system (RTMS) and FACTS devices, self-recovery of district power plants; Artificial intelligence, and, including, neural networks for solving problems of identification and management; expert systems for training and conducting training, early detection and localization of emergency pre-emergency regimes; Adaptive vector control of flexible AC systems for primary and secondary automatic control of voltage and reactive power, optimization of power modes;
Adaptive automatic control for renewable energy sources, including wind, tidal, solar, and in the future, space solar power plants;
Intellectual cybersecurity, capable of providing the required stability of the future Smart Grid energy systems in the context of information confrontation, etc.
III.
In intellectual grids based on Smart Grid, it is advisable to
use ontology (meta-ontology) of cyber-security as a way of
representing knowledge about qualitative characteristics and
quantitative patterns of information confrontation [
The ontology of cybersecurity, according to Thomas
Grubber, is a certain specification of the conceptualization of
the subject area of information confrontation [
Previously, questions of ontological modeling and artificial
intelligence were considered by T. Gruber, N. Guarino, D.
Oberle, and others, and in the Russia by G. S. Pospelov,
D. A. Pospelov, E. V. Popov, L. S. Mussel, A. S. Kleshchev,
I. L. Artemyeva, T. N. Vorozhtsova, D. N. Biryukov,
I. V. Kotenko, A. G. Lomako, and many others [
In the works of A. S. Kleschev. and Artemieva I. L. [
1) On the substantive level, ontology is understood to mean the totality of agreements (definitions of terms of the subject domain, their interpretation, statements that limit the possible meaning of these terms, as well as the interpretation of these statements). Unlike empirical knowledge, these agreements can not be refuted by empirical observations.
2) Ontology, conceptualization, knowledge and reality must be modeled by a single mathematical construction.
3) An explicit correspondence must be established between the properties of the subject domains and the elements of this mathematical construction.
4) The ontology model of each subject area should contain both formal elements and their meaningful interpretation in terms understandable to specialists of this subject area.
5) The ontology and its model should be observable even for complex subject areas with a large number of concepts.
In the works of I.V. Kotenko [
3) Detection of abnormal activity and explicit cyberattacks, as well as illegitimate actions and deviations of users' work from the security policy, prediction of intentions and possible actions of violators; 4) Active response to attempts to implement the actions of violators by automatic reconfiguration of protection components to reflect the actions of violators in real time; 5) Misinformation of the attacker, concealment and camouflage of important resources and processes, "enticement" of the attacker into false (fraudulent) components for the purpose of disclosing and clarifying its purposes, reflexive control over the behavior of the attacker;
6) Monitoring the functioning of the network and monitoring the correctness of the current security policy and network configuration;
7) Support for decision-making on the management of security policies, including on adaptation to subsequent incursions and strengthening of critical defense mechanisms.
In the works of D. Biryukov. and Lomako A.G. [
In 2011, based on the RDF language, basic for the Semantic Web, a general conceptual (reference) model of the Smart Grid was created, containing structured and unstructured information (authors and support of researchers from the Karlsruher Institut für Technologie Institut AIFB). Despite the fact that this ontology was the most complete, the issues of information protection in it, as well as in other Smart Grid ontologies, were not considered.
Russian scientists in [
In the context of information confrontation, a more advanced ontology of cyber security, Smart Grid, is required, which allows to prevent the reduction of power systems to catastrophic consequences.
This formulation of the problem required a significant
revision of the well-known concept of providing information
security for Smart Grid. The point is that modern power
systems, which are complex distributed heterogeneous systems,
do not possess the required stability for targeted operation in
the current and prospective information warfare because of the
high complexity of construction and the potential danger of
undeclared operation of equipment and system-wide software,
including, hypervisors. The means of identifying and complex
neutralizing information and technical impacts combining the
possibilities of joint combined use of technologies for
obtaining unauthorized access, hardware-program bookmarks
and malicious software are still not effective enough [
Moreover, neither traditional means of information protection at the levels: Level 4 - ERP; Level 3 - MES; Level 2 - SCADA; Level 1 - Programmable logic controller (PLC) / Relay protection and automation (RPA); Level 0 - field devices that include traditional means: protection from unauthorized access, firewalling, traffic filtering (Modbus, OPC, IEC 104), detection and prevention of cyberattacks (IDS / IPS), antivirus protection, cryptographic protection of information, analysis security, integrity control and cyber security management in general based on SCIRT / CERT / SOC), nor the known means of ensuring the stability of power systems using backup, calibration and reconfiguration capabilities are no longer suitable for I ensure the required performance of the promising Smart Grid in the conditions of information confrontation.
IV.
CYBER-SECURITY
The analysis of probable scenarios for the purposeful
informational impact on the future Smart Grid energy systems
was conducted with the aim of developing a new ontology of
cybersecurity. The typical structure of the mentioned power
systems is considered and the characteristics of their
vulnerabilities are given. The specifics of the implementation
of security threats and possible risks to the performance of a
typical power system are revealed. The specifics of the
implementation of information and technical impacts on
critical elements of prospective power systems are revealed
[
A critical analysis of existing methods and tools for the
detection and neutralization of information and technology
impacts, including targeted or targeted attacks, APT. The
assessment is made of the suitability of traditional means of
protecting information in power systems for the prevention,
detection and neutralization of information and technology
impacts. The shortcomings of the organization of the means of
providing and monitoring the policy of cybersecurity on the
basis of IEC 62351-8 [
As a result, the ontology of cyber-security of selfrecovering Smart Grid was proposed, which allows describing the organization of self-recovering of perspective energy systems in conditions of information confrontation on the basis of immunity to disturbances by analogy with the immune system of protection of a living organism.
The relevance of the new ontology of cyber security Smart Grid is confirmed by the requirements of the Doctrine of Information Security of Russia (2016), the federal law On the Security of the Critical Information Infrastructure of the Russian Federation (2017), GOSTs of the Federal Agency for Technical Regulation and Metrology (2016) normative and methodological documents (2007) and the order of FSTEC of Russia "On approval of the Requirements for ensuring the protection of information in automated control systems for production and technological processes on critical issues ki important objects ... "(2014) and others.
In this article, the cyber-security ontology of selfrecovering Smart Grid (hereinafter - the ontology of cybersecurity) is understood as the basis for reusable knowledge of a special kind, or the "specification of conceptualization" of such a hard-formalized subject area as ensuring the sustainability of functioning of perspective energy systems in the context of information confrontation. This means that in this area, based on the classification of the basic terms of cybersecurity, it is first necessary to isolate the basic concepts (concepts), and then to determine the connections between them (conceptualization). In this case, the ontology of cybersecurity can be represented both graphically and analytically (for example, a formal grammar and programming language or some mathematical model).
Two methodological approaches were used to develop the ontology of cybersecurity. In the first, for the graphical representation of the ontology of cybersecurity, the IDEF5 Schematic Language is used, and for the analytical description is the text language IDEF5 Elaboration Language. In order to automate the simulation of this ontology of cyber security, a demonstration prototype of the SBONT tool of Knowledge Based Systems, Inc. is used.
Implementation of the first methodological approach took 5 years (2000-2005). Currently, the ontology of cybersecurity contains a description of 800 terms from the field of information security (two volumes with a volume of 1284 pages with text and graphic schemes have been prepared), and are constantly maintained in the current state.
For the current version of the ontology of cybersecurity as the initial data were also used terms and definitions of the following regulations and recommendations of the best practice:
1. Thesaurus of normative documents "The Doctrine of Information Security of Russia" (2016), "The main directions of the state policy in the field of ensuring the safety of the automated control system of the Russian Federation" and the "System of Critical Objects ..." of the Security Council of the Russian Federation.
2. The thesaurus of the Federal Law of the Russian Federation of July 27, 2006, No. 149-FZ "On Information, Information Technologies and Information Protection", Federal Law No. 16-FZ dated February 9, 2007 "On Transport Security", Federal Law No. 256-FZ of July 21, FZ "On the Safety of Fuel and Energy Complex Facilities", Federal Law No. 116-FZ of 21.07.1997 "On Industrial Safety of Hazardous Production Facilities", Federal Law of the Russian Federation No. 170-FZ of 21.11.1995 "On the Use of Atomic Energy", Federal Law " On the Security of the Critical Information Infrastructure. " 2007 FSTEC documents: "Basic model of threats to information security in key information infrastructure systems", "Methodology for determining current threats to information security in key information infrastructure systems", "General requirements for ensuring information security in key information infrastructure systems", "Recommendations for ensuring information security in key information infrastructure systems", "Regulations on the registry of key information infrastructure systems"; draft documents for 2016: "Protection measures in the automated process control system", "Methodology for determining threats to information security in the automated process control system", "Procedure for identifying and eliminating vulnerabilities in the automated process control system", "Procedure for responding to incidents related to the violation of information security".
4. GOST R 53114-2008 "Ensuring information security in
the organization" and GOST R 50922-2006 "Information
security. Basic terms and definitions "; GOST of the Federal
Agency for Technical Regulation and Metrology - Network
communication industrial. Security (cybersecurity) of the
network and system: GOST R 56205-2014 IEC / TS
62443-11-200. Part 1-1. Terminology, conceptual provisions and
models, GOST R IEC 62443-2-1-2015. Part 2-1. Preparation of
the program for ensuring the security (cybersecurity) of the
control system and industrial automation, GOST R 56498-2015
/ IEC / PAS 62443-3: 2008. Part 3. The security (cybersecurity)
of the industrial measurement and control process; GOST R
56545-2015 "Information security. Vulnerabilities of
information systems. Vulnerability Definition Rules (defines
the content of vulnerability information that security control
vendors should include in their solution database, while the
document takes into account existing practices and
vulnerability description tools such as Common Weakness
Enumeration (CWE), the formal language the Open
Vulnerability and Assessment Language (OVAL), the
Common Vulnerability Scoring System (CVSS) vulnerability
assessment methodology; GOST R 56546-2015 "Information
security. Vulnerabilities of information systems. Classification
of vulnerabilities» (defines the most common types of
vulnerabilities, allowing to unify the terminology used by
pentester) [
5. Best practice: ISO / IEC 27000 standards in the general
principles of ensuring the safety of digital control systems,
including ISO / IEC 27032: 2012 "Guidelines for
Cybersecurity" and ISO / IEC 27000 "Information technology.
Methods of ensuring safety. Information security management
systems. General overview and terminology "; IEC TC57
standards: IEC 61850, IEC60870, IEC 62351 regarding the
safety of communication protocols; standard INL Cyber
Security Procurement Language 2008 [
6. Recommendations: NIST-800-82 r.2 "Guide to Industrial Control Systems (ICS Security) - Guide for the security of process control systems" dated 05.2015, Control Systems Security Program / National Cyber Security Division Recommendations for the developers of the standard), IEC 62443 and ISA 62443 (documents of the International Electrotechnical Commission (IEC) and 99 of the Committee for the Development of Safety Standards of the Automated Automation System (ISA) of the International Automation Society (ISA), NERC CIP (Critical Infrastructure Protection) security (NERC), Departament of Homeland Security: Cyber Security Procurement for ICS, Developments of US-CERT (manuals, models of threats and infringers, rules for responding to cybercriminal, vulnerability databases, etc.)
The development of this ontology of cybersecurity was carried out in stages: 1) defining the context of the ontology of cybersecurity; 2) data collection - definition of the sources of terms and selection of terms for the ontology of cybersecurity; 3) data analysis - definition of the main terms and terms of elements, relationships, verbal description of terms; 4) development of ontology of cybersecurity - creation of a schematic and analytical description of the mentioned ontology;
5) validation of the ontology of cyber-security - checking the completeness and correctness of the ontology, compliance with the original requirements.
The ontology of cybersecurity is represented by graphical schemes in the language of IDEF5 Schematic Language (524 schemes) schemes and corresponding analytical descriptions in the text language of IDEF5 Elaboration Language. The above analytical descriptions of the ontology of cybersecurity are performed in accordance with the previously developed methodology:
1) entering the notation of basic and auxiliary terms of cybersecurity;
2) explanation of the terms-elements with the help of unrelated types; 3) assigning to each term-element a unique identifier; 4) definition of input and output links for each term;
7) if necessary, updating and clarifying the descriptions.
In the second methodological approach, the recommendations of the W3C consortium (The World Wide Web Consortium) are used to represent the ontology of cybersecurity in the context of the semantic web (web 3.0). The second approach took 4 years (2006-2010). To describe the hierarchy of possible Smart Grid cyber-security ontologies with memory, OWL is used, which provides a detailed description of ontology classes, individuals belonging to these classes, and the existing relationships between them. This language extends the capabilities of the RDF language, which provides an opportunity to operate with the basic "subjectpredicate-object" structures, as well as the RDFS language that defines the basic structures and relationships between classes and individuals. At the same time, to ensure the possibility of describing the complex connections between individuals on the ontology of cyber-security Smart Grid, the variant of the OWL DL language is used.
This allowed us to use enumerated types to describe fixed vocabulary structures of the knowledge base of the domain, define multiple links to define many-to-many relationships, and apply logical (Boolean) combinations of classes to define the connections of the complex structure of the Smart Grid ontology of cyber-security with memory. It has been shown that the OWL language allows you to specify different representations of the mentioned ontology of cybersecurity.
It was decided to use the OWL representation in XML syntax as the most common and convenient for automatic processing and analysis of the texts of ontologies of cybersecurity by appropriate software tools. An example of a description of the ontology of cybersecurity using this syntax is given (Table 1).
Integration of separate parts of the cyber-ontology ontology involves the inclusion of ontologies into each other at the level of the language (the owl: imports design). This allowed us to describe the basic concepts, connections and individuals related to the named subject area.
To dynamically expand and modify the knowledge base, a description of the rules for building connections in the SWRL language, which is integrated into ontologies formed in OWL, is used. The rules are used to describe the dynamic relationships between individuals ontologies that arise when certain conditions exist.
For example, such relationships can describe the applicability of the method for solving the problem of ensuring the required stability of the Smart Grid in the conditions of information confrontation, depending on the characteristics of the input data. Using the construction of dynamic relationships in conjunction with the inclusion of ontology makes it possible to implement a partial logical inference already at the level of interpretation of the ontological structure. To do this, a set of active facts, formed in the process of interaction with the user, is formalized as a separate ontology using the inclusion of a basic ontological structure. Interpretation of the received structure allows to carry out the analysis of the basic ontological structure taking into account the entered facts. <!-- Field of knowledge--> <owl:Class rdf:ID="FieldOfKnowledge"/>
<!-- Solution method--> <owl:Class rdf:ID="Method"/>
<!-- Task--> <owl:Class rdf:ID="Problem"/> <!-- A set of data (input or output)-->
<owl:Class rdf:ID="DataSet"/> <!-- Generalization of the method--> <owl:ObjectProperty rdf:ID="generalizedBy"> <rdf:type rdf:resource="&owl;TransitiveProperty"/> <rdfs:domain rdf:resource="#Method"/> <rdfs:range rdf:resource="#Method"/>
</owl:ObjectProperty> <!-- Parametrization of the method--> <owl:ObjectProperty rdf:ID="hasParameter"> <rdfs:domain>
<owl:Class> <owl:unionOf rdf:parseType="Collection"> <owl:Class rdf:about="#Method"/> <owl:Class rdf:about="#Problem"/> </owl:unionOf> </owl:Class> </rdfs:domain> <rdfs:range rdf:resource="#DataSet"/> </owl:ObjectProperty> <!-- Input parameter--> <owl:ObjectProperty rdf:ID="hasInput">
<rdfs:subPropertyOf rdf:resource="#hasParameter"/>
</owl:ObjectProperty> <!-- Output parameter--> <owl:ObjectProperty rdf:ID="hasOutput">
<rdfs:subPropertyOf rdf:resource="#hasParameter"/> </owl:ObjectProperty>
</rdf:RDF>
To perform queries on the ontological structure, the SPARQL language is used, which allows using the existing ontological interpretation tools to analyze the Smart Grid ontology of cyber security with memory (including the construction of dynamic rule relationships). An example of a query is shown in Table 2.
SELECT ?E ?L ?C WHERE { ?E rdf:type escience:DataSet .
?E rdfs:label ?L .
OPTIONAL {?E rdfs:comment ?C} . nano:Hf escience:hasInput ?E .
?E escience:isValue ?V . ?V rdf:type escience:SelectedValue
} V.
Here is a possible structure of the ontology of cybersecurity for describing the set of knowledge used in organizing the self-recovering of the Smart Grid in an information confrontation. This structure was tested in 2012 in joint studies of the scientific schools of cybersecurity LETI, ITMO and the faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University.
In the ontology we distinguish two main layers: the
description of concepts (classes) and individuals that
implement concepts [
Formally, the ontology class layer is defined as a graph
O C, R ~ ~ ~ O C, R where C – is the set of classes, R – is the set of abstract relations connecting classes.
Similarly, a layer of individuals ontology is defined as a graph
~ ~ where C – is the set of individuals, and R – is the set of relations between individuals. Thus for each element layer of individuals identified: a) generalization ratio ~ gn(C) : C C
~ gn(R) : R R which determines the relationship of individuals and the connections between them with the corresponding classes and class relationships;
b) "guard condition", determining the applicability of the elements in these conditions ~ gc(C) (F ) : C {0,1}
~ gc(R) (F ) : R {0,1} where F – is the set of active facts defined for the current task; c) criterion estimation function k (C) (F ) : c~ C | gc(C) (c~) 1 (C)
~ k (R) (F ) : ~r R | gc(R) (~r) 1 (R) ~ where (C) and (R), respectively, the space of criteria for evaluating individuals and the relationships between them.
The inference block allows us to determine the way of solving the problem as a tuple S = (s1, s2 ... sN) of a fixed structure whose i-th element is a set of the form si c~ C | gn(C) (c~) ci
~ where the sequence of classes ci C and requirements for sets si determines the overall structure of the solution. To evaluate the solution constructed by the criteria system, graph analysis is used ~ ~ ~ ~ ~ O' C ', R' : C ' si CS i where
C~S c~S | c~S si , c~1 si : rch(c~S , c~1 )
i i it is an attached class system,
rch(c~1, c~2 ) it is the ratio of the reachable on the graph.
The estimation is carried out in the space of criteria , defined by the intersection of the sets of criteria describing the spaces (C) and (R).
A possible scheme for the formation of immunity to disturbances is shown (see Fig. 1 and Fig 2).
ServiceImplementation - a copy of the service, available as part of the software package;
DataSet - a set of input or output data for a given method or task;
Value - the size of the domain used as input and output data for solving problems. There are two specific classes of quantities that differ in the way they are assigned:
FileExtractedValue - retrieved from the files of the value. The extraction method is described as a class (in the component source code) that implements the IFileValueExtractor standard interface.
SelectedValue - values selected from the list of available. The list of available values is specified in the ontology by individuals belonging to the subclasses of the SelectionDictionary class.
extraction. values available for
The structure of the accumulated immunity database is specified by the ADO.NET Entity Framework model. To organize access to the database, a library is built that provides access to the entity instances stored in the database through the ADO.NET Entity Framework. This approach provided the possibility of accessing the database as a set of interrelated collections storing instances of classes equivalent to database entities. The implementation of direct access to the ontological structure using the Pellet API (RunLib variant) is proposed. The interface implemented by this module includes the following basic methods of working with an ontological structure:
CreateSession () - creates a session, returns the string identifier of the session.
AddOWLModel (<session id>, <ontology>) is an ontological structure extension that is specified in OWL in the form of a separate ontology with possible references to existing elements.
ExecuteQuery (<session id>, <query>) is a request to the ontological structure extended within the current session. The query is specified in SPARQL, the result of which is a string containing the results in XML format.
The general scheme of interaction of RunLib implementation with the ontology interpreter is presented (see Fig. 3).
VI.
CONCLUSIONS
As a result of the work done, the imperfection of the traditional means of monitoring and restoration of the operability of the Smart Grid power systems is revealed. The ways of ensuring the stability of the functioning of power systems under hostile mass information and technical influences are investigated. The goals and objectives of ensuring the sustainability of these prospective power systems in the context of information confrontation are formalized.
Fig. 3. Scheme of interaction between implementations
The choice of a scientific and methodical apparatus suitable for solving the problems of the organization of self-recovering of the Smart Grid was carried out. The use of the theory of formal languages and grammars for the generation and recognition of possible types of mass perturbation structures is proposed. The formation of immunity to destructive disturbances with the use of the results of the theory of control and restoration of the functioning of the Smart Grid
The conceptual bases of self-recovery of perspective energy systems in the conditions of information confrontation are put forward and substantiated and a new, more perfect, ontology of cyber-security of self-recovering Smart Grid is developed.