List of the concepts of the integrated FGCM.
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
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\r\n\tOur rapidly shrinking, interconnected world is experiencing an unprecedented change in the face of digital innovation and emerging globalization. As the world’s population spirals beyond 7.7 billion, international economies are becoming more integrated and mutually dependent upon one other. These interconnected economies are subject to political, social, and cultural expectations unimagined in past decades. Employee skill sets that were in high demand only a few decades ago are now considered obsolete and unnecessary. New occupations are evolving in the face of digital advancement only to be quickly replaced by other emerging occupations more suitable to satisfying transitioning expectations. The changes are endless. Educational systems can no longer educate for today’s jobs. They must educate for tomorrow’s jobs. They must empower the future of their national economies while remaining mindful of the needs of tomorrow’s global economy. They stand at the intersection of globalization and technology. The only thing certain is change.
\r\n\r\n\tThis book is intended to examine the educational issues encountered in such an environment. The book aims to afford a fresh examination of theory, research, and practice into this field of study and to provide the reader with an insight into the challenges, successes, and opportunities encountered by today’s educational institutions.
",isbn:"978-1-83962-470-4",printIsbn:"978-1-83962-469-8",pdfIsbn:"978-1-83962-471-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"0cf6891060eb438d975d250e8b127ed6",bookSignature:"Dr. Lee Waller, Dr. Sharon Waller, Dr. Vongai Mpofu and Dr. Mercy Kurebwa",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9536.jpg",keywords:"Global Skill Sets, Career Development, International Networking, Adult Education, World Education Culture, Modernization, International Standards, Educator Preparation, Educational Technology, Educational Impact, Curriculum Development, Sociocultural Issues",numberOfDownloads:613,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 10th 2020",dateEndSecondStepPublish:"July 1st 2020",dateEndThirdStepPublish:"August 30th 2020",dateEndFourthStepPublish:"November 18th 2020",dateEndFifthStepPublish:"January 17th 2021",remainingDaysToSecondStep:"7 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Prof. Lee Waller completed a Ph.D. in Higher Education Administration from the University of North Texas, he spent 17 years in the American community college system and served for 9 years at Texas A&M University-Commerce before joining the AURAK family.",coeditorOneBiosketch:"Dr.Sharon Waller spent 13 years at a Sherman Independent School District where she served as an educational diagnostician, curriculum coordinator, and teacher, her teaching and research focus on special education, strategic educational leadership, and effective assessment of student learning.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"263301",title:"Dr.",name:"Lee",middleName:null,surname:"Waller",slug:"lee-waller",fullName:"Lee Waller",profilePictureURL:"https://mts.intechopen.com/storage/users/263301/images/system/263301.png",biography:"Prof. Lee Waller completed a Ph.D. in Higher Education Administration from the University of North Texas. 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She joined the AURAK family as Manager of Counseling, Testing, and Disability Services in 2105. Dr. Waller’s teaching and research focus on special education, strategic educational leadership, and effective assessment of student learning.",institutionString:"American University of Ras Al Khaimah",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"299343",title:"Dr.",name:"Vongai",middleName:null,surname:"Mpofu",slug:"vongai-mpofu",fullName:"Vongai Mpofu",profilePictureURL:"https://mts.intechopen.com/storage/users/299343/images/system/299343.jpg",biography:"Dr. Vongai Mpofu is a seasoned Science teacher educator with a strong background in school leadership and science teaching. She holds a Ph. D. in Science Education from the University of Witswatersrand in South Africa and have twelve years of University teaching experience at Bindura University of Science Education (BUSE). She joined University service at BUSE with a wealth of experience of heading several high schools in Zimbabwe. She has been in university leadership as a chairperson of the Department of Science and Mathematics Education as well as the acting Dean of the Faculty of Science Education. Dr. Vongai has a good record for teaching, research, and community engagement as well as qualities of good leadership. She is also engaged in journal editorship and peer reviews. She is actively involved in research and leadership related events inclusive of presenting conference papers and facilitating in research and leadership events. 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Currently, she is working in the Zimbabwe Open University’s Faculty of Education and Department of Educational Studies teaching courses in Educational management at both Bachelors and Masters levels. Mercy Kurebwa holds a Certificate in Education (Morgenster Teachers College), Bachelor’s Degree in Educational Administration, Planning and Policy Studies and a Master’s Degree in Administration, Planning and Policy Studies (University of Zimbabwe) and a Doctor of Philosophy Degree in Education (Zimbabwe Open University). Mercy Kurebwa has published 53 journal articles and has also presented over 20 papers at local and international conferences. The focus of the publications and presentations was on Assessment, Open and Distance Learning (ODel), Early Childhood Education, issues in schools, leadership and a few social issues. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69183",title:"Cybersecurity Risk Analysis of Industrial Automation Systems on the Basis of Cognitive Modeling Technology",doi:"10.5772/intechopen.89215",slug:"cybersecurity-risk-analysis-of-industrial-automation-systems-on-the-basis-of-cognitive-modeling-tech",body:'\nDigital economy, cyber-physical objects, cyberspace, and Internet of Things are concepts that have firmly entered our lives in recent years. As a part of industrial revolution “Industry 4.0,” the face of modern industrial enterprises, which actively use the transition to unmanned production technologies, the integration of information technologies into the most complex production processes, has dramatically changed. In this case, a distinctive feature of production is the close connection of technological networks with the corporate network, as it is necessary both for production management and for administration of industrial networks and systems. Modern technological networks, as a rule, have direct access to the Internet, for example, for maintenance and technical support of industrial automation systems by employees of organizations—contractors. Also, computers of contractors, developers, integrators, and system/network administrators connected to the technological network of the service company from the outside often have free access to the Internet.
\nUnder such conditions, the problem of ensuring the security (cybersecurity) of industrial automation and control systems (IACS) sharply increases. In corporate networks, the object of protection is information and the problem of ensuring the confidentiality of information is primarily addressed. However, in the case of industrial automated control systems, the object of protection is already technological processes (TP), and not ensuring the confidentiality of information comes to the fore, but first of all ensuring the continuity and integrity of the TP itself. Speaking of IACS cybersecurity, the so-called digital attacks (cyber-attacks) are primarily considered associated with exposure to IACS through the control and monitoring devices—controllers, data acquisition and transmission devices, SCADA servers, workstations, telecom equipment, communication lines, etc.
\nThe severity and relevance of IACS cybersecurity problem are confirmed by statistics of recent years, showing a sharp increase in the number of the targeted attacks on industrial networks and systems, as well as an increase in the scale of consequences of these attacks. A vivid example of a large-scale cyber-attack that hit a lot of companies around the world from May 12 to May 15, 2017 is the attack of a network worm—the coder WannaCry [1]. Among the victims of this well-coordinated attack were companies engaged in various types of production, oil refineries, urban infrastructure facilities, and distribution power grids.
\nIn May 2018, VPNFilter malware, which infected at least 500,000 routers and data storage devices in 54 countries around the world, was detected. The purpose of this software is to steal credentials, detect industrial SCADA equipment, and carry out various attacks using infected devices in the botnets. June 2018 was marked by a large-scale cyber-attack on telecommunications companies, communication satellite operators, and defense contractors in the United States and Southeast Asia. During the attack, the attackers infected computers used for managing the communication satellites and collecting geoposition data. According to experts’ opinions, the purpose of the cyber-attack was espionage and data interception from civilian and military communication channels. In total, according to Kaspersky Lab, the share of attacked IACS computers in the world in 2018 increased by 3.2% compared with 2017 and amounted to 47.2% [2].
\nConsidering the seriousness of the current situation and the need to take urgent measures, the international community and information security experts are concerned about finding effective ways to solve the problem of ensuring the security of industrial automated systems. For instance, the European Commission has developed the European Program for Critical Infrastructure Protection. Several international standards for ensuring the cybersecurity of automated process control systems have been proposed and effectively used in world practice, such as NERC Critical Infrastructure Protection, NIST SP 800-82 Guide to Industrial Control Systems Security, ISA/IEC 62443 Industrial Automation and Control Systems Security [3, 4].
\nThe basis of the requirements presented by the ISA/IEC 62443 standards series for ensuring the IACS security is a risk-oriented approach. In accordance with this approach, designing of a management system for a protected IACS involves the following stages:
high-level (preliminary) risk assessment of cyber-attacks effects;
building a reference model of IACS as the protection object, describing the classification of main activities types, technological process, automatic control systems, and other assets;
building an asset model, describing the hierarchy of main objects and assets of IACS, their interaction with networks, key divisions, etc.;
building a reference architecture model, reflecting all basic elements of IACS, telecommunication equipment, communication lines, etc.;
building a zone and conduct model, dividing the protected object into separate security zones;
detailed risk analysis for each selected zone; and
determination of the current security level for each zone and requirements to ensure the target security level of the zone, implemented by the choice of appropriate protection measures.
At the same time, the “bottleneck” of the above normative documents regulating the issues of ensuring IACS cybersecurity is the absence of formalized methods for detailed risk assessment. As the volume of statistical data, development of mathematical models of risk, threats, and security incidents increase, it becomes topical to develop methods and algorithms for quantitative risk assessment, ensuring the possibility of a reasonable choice of IACS devices and the necessary countermeasures both within individual security zones and ensuring the required cybersecurity level of IACS as a whole.
\nA promising way to solve this problem is the use of technology of cognitive modeling, based on construction and analysis of fuzzy grey cognitive maps (FGCM), which has been widely used in recent years [5, 6, 7, 8, 9, 10]. Fuzzy grey (interval) cognitive maps are considered to be a good extension of fuzzy cognitive maps (FCM) family, since they are better suited to experts representations, have a greater interpretability and provide more degrees of freedom to the decision making person on the basis of modeling results.
\nBrief information concerning the “grey” system, the “grey” number, and the “grey” variable is presented below, and the mathematical apparatus of FGCM is considered. Then, on the example of solving the problem of ensuring the integrity of telemetric information in IACS, the technique of assessing the cybersecurity risks with use of FGCM is discussed. In the end of the chapter, the conclusions are drawn and the list of references is given.
\nLet us note one important circumstance. When considering below a specific example of AIS risk assessment using FGCM (Section 2), an approach based on decomposition of the original (integrated) FGCM by disclosing (detailing) the content of its concepts is used, resulting in the set of interconnected local FGCM that characterize certain aspects of AIS risks assessment procedure associated with the features of its subsystems. In ideological plan, this approach is based on the FCM decomposition theory and the algebra of FCM causal transformations proposed in [11, 12]. However, the main difference between the approach described in [12] and our approach is that in [12] the detailed FCM system of a large size comes out as the original FCM, which reduces to a simpler (quotient) FCM by using the operations proposed by the authors. Each concept of this quotient FCM accumulates information on the state of several similar concepts of the original FCM, thus aggregating the corresponding concepts. In our case, on the contrary, the original FGCM has a small dimension, the number of forming its basic concepts corresponds to the number of basic subsystems of the system under study, and the decomposition of FGCM implies a representation of each concept of the original FGCM in the form of independent (local) FGCM, describing the behavior of this concept.
\nThe basis of building FGCM is the Grey Systems Theory, proposed in 1989 by Deng [10]. Within the framework of this theory, objects and systems with high uncertainty, represented by small samples of incomplete and inaccurate data, are studied. Depending on the character of the available information, the studied systems are divided into three types:
“white” systems (the internal structure and the properties of the system are completely known);
“grey” systems (partial information about the system is known); and
“black” systems (the internal structure and the properties of the system are completely unknown).
In accordance with the terminology of the grey systems theory, a fuzzy grey cognitive map is a cognitive model of a system in the form of a directed graph defined with use of the following set:
\nwhere \n
In contrast to the traditional FCM representation, the weights of FGCM connections are set with the use of “grey” (interval) numbers \n
where \n
It is assumed that the change of the concepts state in time is described by equations
\nwhere \n
linear function with saturation:
\n
bipolar sigmoid (hyperbolic tangent):
\n
unipolar sigmoid:
To solve the system of equations (Eq. (3)), it is required to set the initial conditions \n
To determine the stability of the steady-state solution \n
where the value of the positive constant \n
More detailed information on FGCM construction and their learning algorithms can be found in [5, 6, 9].
\nLet us consider the task of assessment of IACS risk on the example of the automated system for collecting, storing, and processing the telemetric information (TMI) of the aviation equipment manufacturer. The current information on the state parameters of on-board aviation systems is continuously collected during the entire period of their operation by the ground services of technical maintenance. The detailed analysis of this information allows the subsequent making the right management decisions on the further operation and modification of on-board aviation systems. Therefore, the task of ensuring the integrity of the mentioned telemetric information under the conditions of possible impact of external and internal threats undoubtedly takes on particular significance.
\nThe generalized structure of the studied territorially distributed automated information system (AIS), providing the collection, storage, and processing of TMI, is presented in Figure 1.
\nThe generalized structure of territorially distributed automated information system for the collection, storage, and processing of TMI. The corresponding subsystems (security zones) are interconnected with the aid of telecommunication channels (conducts).
As the parts of AIS, the following subsystems (zones), combined according to the principle of the unity of functions performed and security requirements for their implementation, are identified:
The subsystem for collecting and storing the primary data at the service stations (Zone 1), which includes:
Element 1—the client part of the SCADA system Web-base;
Element 2—the server part of the SCADA system Web-base;
Element 3—OPC UA client;
Element 4—the temporary storage for accommodating the operative telemetry data accumulated at the object;
Element 5—the server part of the accumulated data transmission to the storage of the aviation equipment manufacturer;
The core of the corporate information network (CIN) of the enterprise-manufacturer (Zone 2), where:
Element 6—the client part for providing access to the server of the service station transferring the accumulated operational data of TMI to the enterprise-manufacturer’s storage;
Element 8—the workstations of administrator and service personnel of the CIN core of the enterprise-manufacturer;
TMI storage subsystem with fault tolerance functions (Zone 3), where:
Element 7—the node of access to TMI data storage at the enterprise-manufacturer;
TMI data processing subsystem with the use of a hierarchy of mathematical models of aviation equipment (Zone 4);
Subsystem of support and implementation of business processes of the enterprise-manufacturer (Zone 5).
The corresponding subsystems (security zones) are interconnected (see Figure 1) with the aid of telecommunication channels (conducts).
\nUsing FGCM as a tool for cognitive modeling, let us turn to the task of analyzing the risks associated with ensuring the TMI integrity in AIS considered above, taking into account the impact of possible external and internal threats to the system. The original (integrated) FGCM for assessing the risks of AIS, serving in this case as the AIS cognitive model of initial approximation (zero decomposition level), is presented in Figure 2.
\nIntegrated FGCM for AIS risk assessment. \n\n⊗\n\n\nW\n∼\n\nij\n\n\n—the grey connection weights indicate an uncertainty in the assessment of the mutual influence of main risk factors and \n\n\nC\n∗\n\n\n—concepts.
The following descriptions are used in Figure 2: superscript (“*”) denotes the affiliation of the concept \n
Concept | \nConcept name | \n
---|---|
\n\n | \nInternal threat to TMI integrity (e.g., due to failures or erroneous actions of staff) | \n
\n\n | \nExternal threat to TMI integrity (e.g., due to attempts of unauthorized access from outside to information) | \n
\n\n | \nModification of TMI data in Zone 1 | \n
\n\n | \nModification of TMI data in Zone 2 | \n
\n\n | \nModification of TMI data in Zone 3 | \n
\n\n | \nModification of TMI data in Zone 5 | \n
\n\n | \nPotential damage caused by violation of TMI integrity in AIS | \n
List of the concepts of the integrated FGCM.
The presence of the grey connection weights \n
The first decomposition level of the original (integrated) FGCM is presented in Figure 3.
\nThe first level of FGCM decomposition to assess the AIS risks.
The following designations of the concepts are used in Figure 3: the superscript (q) of \n
Concept | \nConcept name | \nParent concept | \n
---|---|---|
\n\n | \nInternal threats to the integrity of TMI (concept \n | \n\n\n | \n
\n\n | \nExternal threats to TMI integrity (concept \n | \n\n\n | \n
\n\n | \nAccess to TMI in the client-server SCADA Web-base before adding to the database of TMI operational storage | \n\n\n | \n
\n\n | \nAccess to the database of operative TMI data storage | \n|
\n\n | \nAccess to the network equipment | \n|
\n\n | \nAccess to the module of Web server sending TMI data in the long-term storage of the enterprise-manufacturer | \n|
\n\n | \nAccess to the network infrastructure | \n\n\n | \n
\n\n | \nAccess to the Web client module that implements receiving TMI at the enterprise-manufacturer from remote service stations | \n|
\n\n | \nUnauthorized access to workstation (node 8 in Figure 1) of the core of CIN of the enterprise-manufacturer | \n|
\n\n | \nAccess to the server of equipment status reports generated for users of Zone 4 | \n|
\n\n | \nAccess to TMI in the long-term storage | \n\n\n | \n
\n\n | \nAccess to computing cluster management server of Zone 5 | \n\n\n | \n
\n\n | \nTMI integrity control model | \n
List of the first level decomposition concepts of the FGCM.
\nFigure 4 shows the further decomposition level (the second level) for the concept \n
The second level of FGCM decomposition for assessing AIS risk in Zone 1.
On the scheme, the following designations of the concepts of FGCM second-level decomposition are used: the superscript (q) of the \n
Concept | \nConcept name | \nParent concept | \n
---|---|---|
\n\n | \nAccess to HMI client SCADA | \n\n\n | \n
\n\n | \nAccess to operative TMI data on the client-server part of the SCADA before entering in the operative storage | \n|
\n\n | \nAccess to the client to interact with the OPC UA server | \n\n\n | \n
\n\n | \nAccess to the database of operative TMI storage data | \n
List of second-level decomposition concepts for Zone 1.
The further decomposition of the third level allows us to go to the detailed FGCM, which allows us to take into account the influence of individual vulnerabilities on the potential violation of TMI integrity in the intermediate information processing elements.
\nAs for the concept \n
The third level of FGCM decomposition—the concept\n\n\n\nC\n1\n\n1\n,\n1\n.\n\n\n\n\n
Here, the numbers 1–5 denote the following concepts:
the exploitation of the vulnerability of OS authorization system;
the exploitation of the vulnerability of SCADA Web client;
the exploitation of the vulnerability of OS browser for launching the client part of SCADA;
the exploitation of the vulnerability of access to OS memory;
the exploitation of the vulnerability of OPC UA client authorization system.
Similarly, it is possible to decompose the other concepts of original FGCM for the second decomposition level of Zone 1 presented in Figure 4 (Figures 6–9, Tables 4–6). The corresponding FGCM, revealing the content of the concept \n
Decomposition of the concept \n\n\nC\n2\n\n1\n,\n1\n\n\n\n of FGCM for AIS risk assessment (Zone 1)
Decomposition of the concepts \n\n\nC\n6\n\n1\n,\n3\n\n\n\n and \n\n\nC\n5\n\n1\n,\n4\n\n\n\n of the second level of FGCM decomposition
Decomposition of the concepts \n\n\nC\n3\n\n1\n,\n2\n\n\n\n and \n\n\nC\n4\n\n1\n,\n2\n\n\n\n of FGCM for AIS risk assessment
FGCM concepts states for risk assessment of Zone 1 (the change in the state of concepts over time and the final states of the target concepts of the FGCM, software window form).
Concept | \nConcept name | \nParent concept | \n
---|---|---|
6 | \nExploitation of the vulnerability of authorization system of the primary OS user | \n\n\n | \n
7 | \nExploitation of the vulnerability of access to operating system memory | \n|
8 | \nExploitation of the vulnerability of Java virtual machine | \n|
9 | \nExploitation of the vulnerability of system software of application server for running the SCADA server Web application | \n|
10 | \nThe target concept of access to operative TMI data, which can be modified before adding to the database on the nodes of SCADA client-server type | \n
List of the concepts of the third decomposition level for AIS risks assessment of Zone 1.
Concept | \nConcept name | \nParent concept | \n
---|---|---|
19 | \nExploitation of the vulnerability of authorization system of the main OS user | \n\n\n | \n
20 | \nExploitation of the vulnerability of system software implementing work of Apache Web application server, MySQL DBMS, PHP runtime frameworks to support interactive Web pages | \n|
21 | \nExploitation of the vulnerability of OS memory access | \n|
22 | \nExploitation of the vulnerability of Java Virtual Machine Memory Access | \n|
23 | \nExploitation of the vulnerability of Application Server Software | \n|
24 | \nThe target concept of unauthorized launching of the module for access to the database of operative storage of TMI at service stations | \n|
25 | \nExploitation of the vulnerability of authorization system of the main OS user | \n\n\n | \n
26 | \nExploitation of the vulnerability of access to operating system memory | \n
List of the concepts of the third decomposition level of Zone 1.
Concept | \nConcept name | \nParent concept | \n
---|---|---|
14 | \nExploitation of the vulnerability of authorization system of the main OS user | \n\n\n | \n
15 | \nExploitation of the vulnerability of OS memory access | \n|
16 | \nExploitation of the vulnerability of authorization system of the main DBMS user | \n|
17 | \nExploitation of the vulnerability of DBMS memory access | \n|
18 | \nThe target concept of unauthorized modification of TMI operative data TMI stored in the database | \n|
11 | \nExploitation of the vulnerability of authorization system of the client part of OPC Client UA software | \n\n\n | \n
12 | \nExploitation of the vulnerability of authorization system of the main OS user | \n|
13 | \nExploitation of the vulnerability of OS memory access | \n
List of the concepts of the third level of FGCM decomposition of Zone 1.
Consider the numerical example of risk assessment for the concept \n
Let us assume that while choosing the grey values of FGCM weights, it is necessary to focus on a certain fuzzy scale, which determines the strength of the connections between different concepts (see, e.g., Table 7).
\nLinguistic meaning of connection strength | \nNumeric range | \n
---|---|
Does not affect | \n0 | \n
Very weak | \n(0; 0.15] | \n
Weak | \n(0.15; 0.35] | \n
Average | \n(0.35; 0.6] | \n
Strong | \n(0.6; 0.85] | \n
Very strong | \n(0.85; 1] | \n
Evaluation of the strength of communication between concepts.
Let us further assume that the expert estimated the values of FGCM connections weights in Figure 5 as follows (Table 8).
\nConnection weight | \nThe value of the connection weight | \nGreyness (scatter of assessment) | \n
---|---|---|
\n\n | \n[0.6; 0.75] | \n0.075 | \n
\n\n | \n[0.5; 0.7] | \n0.1 | \n
\n\n | \n[0.5; 0.7] | \n0.1 | \n
\n\n | \n[0.15; 0.3] | \n0.075 | \n
\n\n | \n[0.55; 0.65] | \n0.05 | \n
\n\n | \n[0.35; 0.55] | \n0.1 | \n
\n\n | \n[0.55; 0.65] | \n0.05 | \n
\n\n | \n[0.3; 0.5] | \n0.1 | \n
\n\n | \n[0.15; 0.3] | \n0.075 | \n
\n\n | \n[0.2; 0.45] | \n0.125 | \n
The values of communications FGCM weights.
Let us take a bipolar sigmoid (5) here as an activation function \n
i.e., the steady-states of FGCM will be stable.
\nUsing for calculation the “Cognitive Map Constructor” tool, which is described more detailed in the next section of this chapter, we will estimate the change in the upper and lower boundaries of the state variable \n
\n\n | \n1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n
---|---|---|---|---|---|---|---|---|
\n\n | \n||||||||
\n\n | \n0.36 | \n0.50 | \n0.55 | \n0.57 | \n0.58 | \n0.58 | \n0.58 | \n0.58 | \n
\n\n | \n0 | \n0.125 | \n0.28 | \n0.40 | \n0.48 | \n0.52 | \n0.54 | \n0.55 | \n
\n\n | \n0 | \n0.125 | \n0.24 | \n0.32 | \n0.36 | \n0.38 | \n0.39 | \n0.40 | \n
\n\n | \n0 | \n0.054 | \n0.10 | \n0.13 | \n0.15 | \n0.16 | \n0.16 | \n0.16 | \n
\n\n | \n0 | \n0 | \n0.093 | \n0.23 | \n0.36 | \n0.45 | \n0.50 | \n0.53 | \n
Upper boundaries of concept state estimates
\n\n | \n1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n
---|---|---|---|---|---|---|---|---|
\n\n | \n||||||||
\n\n | \n0.24 | \n0.34 | \n0.39 | \n0.41 | \n0.43 | \n0.43 | \n0.43 | \n0.43 | \n
\n\n | \n0 | \n0.059 | \n0.13 | \n0.18 | \n0.22 | \n0.25 | \n0.27 | \n0.28 | \n
\n\n | \n0 | \n0.059 | \n0.115 | \n0.16 | \n0.18 | \n0.19 | \n0.20 | \n0.20 | \n
\n\n | \n0 | \n0.018 | \n0.034 | \n0.046 | \n0.052 | \n0.058 | \n0.06 | \n0.06 | \n
\n\n | \n0 | \n0 | \n0.034 | \n0.087 | \n0.14 | \n0.18 | \n0.21 | \n0.24 | \n
Lower boundaries of concept state estimates.
As a result, the steady-state value of the grey state vector \n
and the final value for the target concept state is determined by the grey number \n
Consider the state of the target concept \n
Risk assessment because of violation of the integrity of TMI information is defined as \n
To reduce the potential damage from the violation of TMI integrity, a monitoring system, deployed as a protected container in Zone 5, is used. In Figure 3, this information protection tool is designated as a TMI integrity monitoring model—concept \n
The concept of TMI integrity monitoring system as a whole has some peculiarities:
Simulated parameters of the aviation engine operation and TMI can be presented in the form of multidimensional technological time series;
Monitoring the TMI integrity is based on the analysis of the consistency of the behavior of parameters obtained by using the model of complex technical object, and taken from the on-board aircraft systems;
The output of the monitoring system is the evaluation of conditional probability of the events of data integrity violation events.
Risk value estimate due to violation of TMI information integrity after applying the tool based on the integrity monitoring model is \n
Due to the significant amount of computation when working with FGCM containing a large number of concepts, it was necessary to develop a software tool to automate cognitive modeling with use of FGCM. The change in the state of concepts over time and the final states of the target concepts of the FGCM, calculated in the developed software tool, are presented in Figure 9.
\nTo improve an efficiency of risk analysis and management with use of FGCM, the special software tool “Cognitive Map Constructor” was developed. This software allows us to build and edit FGCM, use them to carry out the security risk analysis, and justify the choice of the necessary countermeasures from the given user-specified set. As a result, a diagram of risk assessment is built under various scenarios of countermeasures’ implementation and threats’ realization.
\nBesides supporting the FGCM with the installation of connections weights in the form of the upper and lower boundaries, the software allows us the use of linguistic terms of fuzzy logic, as well as setting the weights in the form of “white” crisp numbers. The software has the interface implemented in HTML using CSS, which allows displaying the FGCM and all the necessary accompanying information by the concepts and connections, and also is able to work on any graphical operating system that has a current Web browser.
\nThere are five kinds of concepts which are used in FGCM: threats, information assets, intermediate concepts, targets, and countermeasures, which can be marked by different colors for convenience and clarity.
\nThe set of the options depends on the type of the concept, but in most cases its name is specified with description, as well as its current state. In the case when the weights of all connections, pointing to the concept, are assumed to be equal, one can mark the option “Imposed weight” and set the desired value. For countermeasures, it is permissible to indicate which of existing countermeasure it is, that allows realizing situations when one countermeasure acts on several connections at once.
\nTo establish the relationships between the concepts, it is necessary to click on the button “Placement” of the action group “Connections” in the tool window. After that, the connections are located by pressing consecutively on the initial and final element. The located countermeasures and initial states of the concepts can be adjusted and combined, creating the different scenarios that allow us to compare the effectiveness of countermeasures.
\n\nFigures 10 and 11 show the FGCM risk estimates built in the “Cognitive Map Constructor.”
\nFGCM for risk assessment of data collection and storage subsystem at the service stations (Zone 1) (software window form).
FGCM for risk assessment in the core of the CIN (Zone 2) and TMI (software window form).
Thus, the developed software “Cognitive Map Constructor” allows evaluating the effectiveness of the use of the TMI integrity monitoring system in the protection of telemetric information from the effects of external and internal threats.
\nA promising way to solve the problem of assessing the cybersecurity risks of industrial automated systems is to model the threats realization scenarios using the tools of topological analysis of the system security and cognitive modeling with the aid of Fuzzy Grey Cognitive Maps.
\nAt the basis of this approach, the construction of original FGCM is proposed to assess the risk of automated control system with the following decomposition of FGCM into the number of cognitive maps of the next level of detail (the same as it is done in IDEF0 Functional Modeling technology). The features of construction of this procedure are discussed in this chapter in relation to the task of ensuring the telemetry information integrity in the industrial automated system for collecting, storing, and processing information on the conditions of on-board aviation systems. It is shown that the use of FGCM allows us to obtain more reliable estimates of security risk factors with account of the possible variations of the available actual data and expert opinions.
\nTo automate the proposed risk assessment procedure in the considered system for collecting, storing, and processing telemetry information with use of FGCM, the software tool “Cognitive Map Constructor” was developed, which can be used for identifying the most dangerous vulnerabilities in the system and evaluating the effectiveness of various measures (countermeasures) realization for telemetric information protection from the impact of external and internal threats.
\nThe reported study was funded by RFBR according to the research Project No. 18-00-00238 “Decision support methods and models for innovative project management based on knowledge engineering.”
\nDigital economy, cyber-physical objects, cyberspace, and Internet of Things are concepts that have firmly entered our lives in recent years. As a part of industrial revolution “Industry 4.0,” the face of modern industrial enterprises, which actively use the transition to unmanned production technologies, the integration of information technologies into the most complex production processes, has dramatically changed. In this case, a distinctive feature of production is the close connection of technological networks with the corporate network, as it is necessary both for production management and for administration of industrial networks and systems. Modern technological networks, as a rule, have direct access to the Internet, for example, for maintenance and technical support of industrial automation systems by employees of organizations—contractors. Also, computers of contractors, developers, integrators, and system/network administrators connected to the technological network of the service company from the outside often have free access to the Internet.
\nUnder such conditions, the problem of ensuring the security (cybersecurity) of industrial automation and control systems (IACS) sharply increases. In corporate networks, the object of protection is information and the problem of ensuring the confidentiality of information is primarily addressed. However, in the case of industrial automated control systems, the object of protection is already technological processes (TP), and not ensuring the confidentiality of information comes to the fore, but first of all ensuring the continuity and integrity of the TP itself. Speaking of IACS cybersecurity, the so-called digital attacks (cyber-attacks) are primarily considered associated with exposure to IACS through the control and monitoring devices—controllers, data acquisition and transmission devices, SCADA servers, workstations, telecom equipment, communication lines, etc.
\nThe severity and relevance of IACS cybersecurity problem are confirmed by statistics of recent years, showing a sharp increase in the number of the targeted attacks on industrial networks and systems, as well as an increase in the scale of consequences of these attacks. A vivid example of a large-scale cyber-attack that hit a lot of companies around the world from May 12 to May 15, 2017 is the attack of a network worm—the coder WannaCry [1]. Among the victims of this well-coordinated attack were companies engaged in various types of production, oil refineries, urban infrastructure facilities, and distribution power grids.
\nIn May 2018, VPNFilter malware, which infected at least 500,000 routers and data storage devices in 54 countries around the world, was detected. The purpose of this software is to steal credentials, detect industrial SCADA equipment, and carry out various attacks using infected devices in the botnets. June 2018 was marked by a large-scale cyber-attack on telecommunications companies, communication satellite operators, and defense contractors in the United States and Southeast Asia. During the attack, the attackers infected computers used for managing the communication satellites and collecting geoposition data. According to experts’ opinions, the purpose of the cyber-attack was espionage and data interception from civilian and military communication channels. In total, according to Kaspersky Lab, the share of attacked IACS computers in the world in 2018 increased by 3.2% compared with 2017 and amounted to 47.2% [2].
\nConsidering the seriousness of the current situation and the need to take urgent measures, the international community and information security experts are concerned about finding effective ways to solve the problem of ensuring the security of industrial automated systems. For instance, the European Commission has developed the European Program for Critical Infrastructure Protection. Several international standards for ensuring the cybersecurity of automated process control systems have been proposed and effectively used in world practice, such as NERC Critical Infrastructure Protection, NIST SP 800-82 Guide to Industrial Control Systems Security, ISA/IEC 62443 Industrial Automation and Control Systems Security [3, 4].
\nThe basis of the requirements presented by the ISA/IEC 62443 standards series for ensuring the IACS security is a risk-oriented approach. In accordance with this approach, designing of a management system for a protected IACS involves the following stages:
high-level (preliminary) risk assessment of cyber-attacks effects;
building a reference model of IACS as the protection object, describing the classification of main activities types, technological process, automatic control systems, and other assets;
building an asset model, describing the hierarchy of main objects and assets of IACS, their interaction with networks, key divisions, etc.;
building a reference architecture model, reflecting all basic elements of IACS, telecommunication equipment, communication lines, etc.;
building a zone and conduct model, dividing the protected object into separate security zones;
detailed risk analysis for each selected zone; and
determination of the current security level for each zone and requirements to ensure the target security level of the zone, implemented by the choice of appropriate protection measures.
At the same time, the “bottleneck” of the above normative documents regulating the issues of ensuring IACS cybersecurity is the absence of formalized methods for detailed risk assessment. As the volume of statistical data, development of mathematical models of risk, threats, and security incidents increase, it becomes topical to develop methods and algorithms for quantitative risk assessment, ensuring the possibility of a reasonable choice of IACS devices and the necessary countermeasures both within individual security zones and ensuring the required cybersecurity level of IACS as a whole.
\nA promising way to solve this problem is the use of technology of cognitive modeling, based on construction and analysis of fuzzy grey cognitive maps (FGCM), which has been widely used in recent years [5, 6, 7, 8, 9, 10]. Fuzzy grey (interval) cognitive maps are considered to be a good extension of fuzzy cognitive maps (FCM) family, since they are better suited to experts representations, have a greater interpretability and provide more degrees of freedom to the decision making person on the basis of modeling results.
\nBrief information concerning the “grey” system, the “grey” number, and the “grey” variable is presented below, and the mathematical apparatus of FGCM is considered. Then, on the example of solving the problem of ensuring the integrity of telemetric information in IACS, the technique of assessing the cybersecurity risks with use of FGCM is discussed. In the end of the chapter, the conclusions are drawn and the list of references is given.
\nLet us note one important circumstance. When considering below a specific example of AIS risk assessment using FGCM (Section 2), an approach based on decomposition of the original (integrated) FGCM by disclosing (detailing) the content of its concepts is used, resulting in the set of interconnected local FGCM that characterize certain aspects of AIS risks assessment procedure associated with the features of its subsystems. In ideological plan, this approach is based on the FCM decomposition theory and the algebra of FCM causal transformations proposed in [11, 12]. However, the main difference between the approach described in [12] and our approach is that in [12] the detailed FCM system of a large size comes out as the original FCM, which reduces to a simpler (quotient) FCM by using the operations proposed by the authors. Each concept of this quotient FCM accumulates information on the state of several similar concepts of the original FCM, thus aggregating the corresponding concepts. In our case, on the contrary, the original FGCM has a small dimension, the number of forming its basic concepts corresponds to the number of basic subsystems of the system under study, and the decomposition of FGCM implies a representation of each concept of the original FGCM in the form of independent (local) FGCM, describing the behavior of this concept.
\nThe basis of building FGCM is the Grey Systems Theory, proposed in 1989 by Deng [10]. Within the framework of this theory, objects and systems with high uncertainty, represented by small samples of incomplete and inaccurate data, are studied. Depending on the character of the available information, the studied systems are divided into three types:
“white” systems (the internal structure and the properties of the system are completely known);
“grey” systems (partial information about the system is known); and
“black” systems (the internal structure and the properties of the system are completely unknown).
In accordance with the terminology of the grey systems theory, a fuzzy grey cognitive map is a cognitive model of a system in the form of a directed graph defined with use of the following set:
\nwhere \n
In contrast to the traditional FCM representation, the weights of FGCM connections are set with the use of “grey” (interval) numbers \n
where \n
It is assumed that the change of the concepts state in time is described by equations
\nwhere \n
linear function with saturation:
\n
bipolar sigmoid (hyperbolic tangent):
\n
unipolar sigmoid:
To solve the system of equations (Eq. (3)), it is required to set the initial conditions \n
To determine the stability of the steady-state solution \n
where the value of the positive constant \n
More detailed information on FGCM construction and their learning algorithms can be found in [5, 6, 9].
\nLet us consider the task of assessment of IACS risk on the example of the automated system for collecting, storing, and processing the telemetric information (TMI) of the aviation equipment manufacturer. The current information on the state parameters of on-board aviation systems is continuously collected during the entire period of their operation by the ground services of technical maintenance. The detailed analysis of this information allows the subsequent making the right management decisions on the further operation and modification of on-board aviation systems. Therefore, the task of ensuring the integrity of the mentioned telemetric information under the conditions of possible impact of external and internal threats undoubtedly takes on particular significance.
\nThe generalized structure of the studied territorially distributed automated information system (AIS), providing the collection, storage, and processing of TMI, is presented in Figure 1.
\nThe generalized structure of territorially distributed automated information system for the collection, storage, and processing of TMI. The corresponding subsystems (security zones) are interconnected with the aid of telecommunication channels (conducts).
As the parts of AIS, the following subsystems (zones), combined according to the principle of the unity of functions performed and security requirements for their implementation, are identified:
The subsystem for collecting and storing the primary data at the service stations (Zone 1), which includes:
Element 1—the client part of the SCADA system Web-base;
Element 2—the server part of the SCADA system Web-base;
Element 3—OPC UA client;
Element 4—the temporary storage for accommodating the operative telemetry data accumulated at the object;
Element 5—the server part of the accumulated data transmission to the storage of the aviation equipment manufacturer;
The core of the corporate information network (CIN) of the enterprise-manufacturer (Zone 2), where:
Element 6—the client part for providing access to the server of the service station transferring the accumulated operational data of TMI to the enterprise-manufacturer’s storage;
Element 8—the workstations of administrator and service personnel of the CIN core of the enterprise-manufacturer;
TMI storage subsystem with fault tolerance functions (Zone 3), where:
Element 7—the node of access to TMI data storage at the enterprise-manufacturer;
TMI data processing subsystem with the use of a hierarchy of mathematical models of aviation equipment (Zone 4);
Subsystem of support and implementation of business processes of the enterprise-manufacturer (Zone 5).
The corresponding subsystems (security zones) are interconnected (see Figure 1) with the aid of telecommunication channels (conducts).
\nUsing FGCM as a tool for cognitive modeling, let us turn to the task of analyzing the risks associated with ensuring the TMI integrity in AIS considered above, taking into account the impact of possible external and internal threats to the system. The original (integrated) FGCM for assessing the risks of AIS, serving in this case as the AIS cognitive model of initial approximation (zero decomposition level), is presented in Figure 2.
\nIntegrated FGCM for AIS risk assessment. \n\n⊗\n\n\nW\n∼\n\nij\n\n\n—the grey connection weights indicate an uncertainty in the assessment of the mutual influence of main risk factors and \n\n\nC\n∗\n\n\n—concepts.
The following descriptions are used in Figure 2: superscript (“*”) denotes the affiliation of the concept \n
Concept | \nConcept name | \n
---|---|
\n\n | \nInternal threat to TMI integrity (e.g., due to failures or erroneous actions of staff) | \n
\n\n | \nExternal threat to TMI integrity (e.g., due to attempts of unauthorized access from outside to information) | \n
\n\n | \nModification of TMI data in Zone 1 | \n
\n\n | \nModification of TMI data in Zone 2 | \n
\n\n | \nModification of TMI data in Zone 3 | \n
\n\n | \nModification of TMI data in Zone 5 | \n
\n\n | \nPotential damage caused by violation of TMI integrity in AIS | \n
List of the concepts of the integrated FGCM.
The presence of the grey connection weights \n
The first decomposition level of the original (integrated) FGCM is presented in Figure 3.
\nThe first level of FGCM decomposition to assess the AIS risks.
The following designations of the concepts are used in Figure 3: the superscript (q) of \n
Concept | \nConcept name | \nParent concept | \n
---|---|---|
\n\n | \nInternal threats to the integrity of TMI (concept \n | \n\n\n | \n
\n\n | \nExternal threats to TMI integrity (concept \n | \n\n\n | \n
\n\n | \nAccess to TMI in the client-server SCADA Web-base before adding to the database of TMI operational storage | \n\n\n | \n
\n\n | \nAccess to the database of operative TMI data storage | \n|
\n\n | \nAccess to the network equipment | \n|
\n\n | \nAccess to the module of Web server sending TMI data in the long-term storage of the enterprise-manufacturer | \n|
\n\n | \nAccess to the network infrastructure | \n\n\n | \n
\n\n | \nAccess to the Web client module that implements receiving TMI at the enterprise-manufacturer from remote service stations | \n|
\n\n | \nUnauthorized access to workstation (node 8 in Figure 1) of the core of CIN of the enterprise-manufacturer | \n|
\n\n | \nAccess to the server of equipment status reports generated for users of Zone 4 | \n|
\n\n | \nAccess to TMI in the long-term storage | \n\n\n | \n
\n\n | \nAccess to computing cluster management server of Zone 5 | \n\n\n | \n
\n\n | \nTMI integrity control model | \n
List of the first level decomposition concepts of the FGCM.
\nFigure 4 shows the further decomposition level (the second level) for the concept \n
The second level of FGCM decomposition for assessing AIS risk in Zone 1.
On the scheme, the following designations of the concepts of FGCM second-level decomposition are used: the superscript (q) of the \n
Concept | \nConcept name | \nParent concept | \n
---|---|---|
\n\n | \nAccess to HMI client SCADA | \n\n\n | \n
\n\n | \nAccess to operative TMI data on the client-server part of the SCADA before entering in the operative storage | \n|
\n\n | \nAccess to the client to interact with the OPC UA server | \n\n\n | \n
\n\n | \nAccess to the database of operative TMI storage data | \n
List of second-level decomposition concepts for Zone 1.
The further decomposition of the third level allows us to go to the detailed FGCM, which allows us to take into account the influence of individual vulnerabilities on the potential violation of TMI integrity in the intermediate information processing elements.
\nAs for the concept \n
The third level of FGCM decomposition—the concept\n\n\n\nC\n1\n\n1\n,\n1\n.\n\n\n\n\n
Here, the numbers 1–5 denote the following concepts:
the exploitation of the vulnerability of OS authorization system;
the exploitation of the vulnerability of SCADA Web client;
the exploitation of the vulnerability of OS browser for launching the client part of SCADA;
the exploitation of the vulnerability of access to OS memory;
the exploitation of the vulnerability of OPC UA client authorization system.
Similarly, it is possible to decompose the other concepts of original FGCM for the second decomposition level of Zone 1 presented in Figure 4 (Figures 6–9, Tables 4–6). The corresponding FGCM, revealing the content of the concept \n
Decomposition of the concept \n\n\nC\n2\n\n1\n,\n1\n\n\n\n of FGCM for AIS risk assessment (Zone 1)
Decomposition of the concepts \n\n\nC\n6\n\n1\n,\n3\n\n\n\n and \n\n\nC\n5\n\n1\n,\n4\n\n\n\n of the second level of FGCM decomposition
Decomposition of the concepts \n\n\nC\n3\n\n1\n,\n2\n\n\n\n and \n\n\nC\n4\n\n1\n,\n2\n\n\n\n of FGCM for AIS risk assessment
FGCM concepts states for risk assessment of Zone 1 (the change in the state of concepts over time and the final states of the target concepts of the FGCM, software window form).
Concept | \nConcept name | \nParent concept | \n
---|---|---|
6 | \nExploitation of the vulnerability of authorization system of the primary OS user | \n\n\n | \n
7 | \nExploitation of the vulnerability of access to operating system memory | \n|
8 | \nExploitation of the vulnerability of Java virtual machine | \n|
9 | \nExploitation of the vulnerability of system software of application server for running the SCADA server Web application | \n|
10 | \nThe target concept of access to operative TMI data, which can be modified before adding to the database on the nodes of SCADA client-server type | \n
List of the concepts of the third decomposition level for AIS risks assessment of Zone 1.
Concept | \nConcept name | \nParent concept | \n
---|---|---|
19 | \nExploitation of the vulnerability of authorization system of the main OS user | \n\n\n | \n
20 | \nExploitation of the vulnerability of system software implementing work of Apache Web application server, MySQL DBMS, PHP runtime frameworks to support interactive Web pages | \n|
21 | \nExploitation of the vulnerability of OS memory access | \n|
22 | \nExploitation of the vulnerability of Java Virtual Machine Memory Access | \n|
23 | \nExploitation of the vulnerability of Application Server Software | \n|
24 | \nThe target concept of unauthorized launching of the module for access to the database of operative storage of TMI at service stations | \n|
25 | \nExploitation of the vulnerability of authorization system of the main OS user | \n\n\n | \n
26 | \nExploitation of the vulnerability of access to operating system memory | \n
List of the concepts of the third decomposition level of Zone 1.
Concept | \nConcept name | \nParent concept | \n
---|---|---|
14 | \nExploitation of the vulnerability of authorization system of the main OS user | \n\n\n | \n
15 | \nExploitation of the vulnerability of OS memory access | \n|
16 | \nExploitation of the vulnerability of authorization system of the main DBMS user | \n|
17 | \nExploitation of the vulnerability of DBMS memory access | \n|
18 | \nThe target concept of unauthorized modification of TMI operative data TMI stored in the database | \n|
11 | \nExploitation of the vulnerability of authorization system of the client part of OPC Client UA software | \n\n\n | \n
12 | \nExploitation of the vulnerability of authorization system of the main OS user | \n|
13 | \nExploitation of the vulnerability of OS memory access | \n
List of the concepts of the third level of FGCM decomposition of Zone 1.
Consider the numerical example of risk assessment for the concept \n
Let us assume that while choosing the grey values of FGCM weights, it is necessary to focus on a certain fuzzy scale, which determines the strength of the connections between different concepts (see, e.g., Table 7).
\nLinguistic meaning of connection strength | \nNumeric range | \n
---|---|
Does not affect | \n0 | \n
Very weak | \n(0; 0.15] | \n
Weak | \n(0.15; 0.35] | \n
Average | \n(0.35; 0.6] | \n
Strong | \n(0.6; 0.85] | \n
Very strong | \n(0.85; 1] | \n
Evaluation of the strength of communication between concepts.
Let us further assume that the expert estimated the values of FGCM connections weights in Figure 5 as follows (Table 8).
\nConnection weight | \nThe value of the connection weight | \nGreyness (scatter of assessment) | \n
---|---|---|
\n\n | \n[0.6; 0.75] | \n0.075 | \n
\n\n | \n[0.5; 0.7] | \n0.1 | \n
\n\n | \n[0.5; 0.7] | \n0.1 | \n
\n\n | \n[0.15; 0.3] | \n0.075 | \n
\n\n | \n[0.55; 0.65] | \n0.05 | \n
\n\n | \n[0.35; 0.55] | \n0.1 | \n
\n\n | \n[0.55; 0.65] | \n0.05 | \n
\n\n | \n[0.3; 0.5] | \n0.1 | \n
\n\n | \n[0.15; 0.3] | \n0.075 | \n
\n\n | \n[0.2; 0.45] | \n0.125 | \n
The values of communications FGCM weights.
Let us take a bipolar sigmoid (5) here as an activation function \n
i.e., the steady-states of FGCM will be stable.
\nUsing for calculation the “Cognitive Map Constructor” tool, which is described more detailed in the next section of this chapter, we will estimate the change in the upper and lower boundaries of the state variable \n
\n\n | \n1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n
---|---|---|---|---|---|---|---|---|
\n\n | \n||||||||
\n\n | \n0.36 | \n0.50 | \n0.55 | \n0.57 | \n0.58 | \n0.58 | \n0.58 | \n0.58 | \n
\n\n | \n0 | \n0.125 | \n0.28 | \n0.40 | \n0.48 | \n0.52 | \n0.54 | \n0.55 | \n
\n\n | \n0 | \n0.125 | \n0.24 | \n0.32 | \n0.36 | \n0.38 | \n0.39 | \n0.40 | \n
\n\n | \n0 | \n0.054 | \n0.10 | \n0.13 | \n0.15 | \n0.16 | \n0.16 | \n0.16 | \n
\n\n | \n0 | \n0 | \n0.093 | \n0.23 | \n0.36 | \n0.45 | \n0.50 | \n0.53 | \n
Upper boundaries of concept state estimates
\n\n | \n1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n
---|---|---|---|---|---|---|---|---|
\n\n | \n||||||||
\n\n | \n0.24 | \n0.34 | \n0.39 | \n0.41 | \n0.43 | \n0.43 | \n0.43 | \n0.43 | \n
\n\n | \n0 | \n0.059 | \n0.13 | \n0.18 | \n0.22 | \n0.25 | \n0.27 | \n0.28 | \n
\n\n | \n0 | \n0.059 | \n0.115 | \n0.16 | \n0.18 | \n0.19 | \n0.20 | \n0.20 | \n
\n\n | \n0 | \n0.018 | \n0.034 | \n0.046 | \n0.052 | \n0.058 | \n0.06 | \n0.06 | \n
\n\n | \n0 | \n0 | \n0.034 | \n0.087 | \n0.14 | \n0.18 | \n0.21 | \n0.24 | \n
Lower boundaries of concept state estimates.
As a result, the steady-state value of the grey state vector \n
and the final value for the target concept state is determined by the grey number \n
Consider the state of the target concept \n
Risk assessment because of violation of the integrity of TMI information is defined as \n
To reduce the potential damage from the violation of TMI integrity, a monitoring system, deployed as a protected container in Zone 5, is used. In Figure 3, this information protection tool is designated as a TMI integrity monitoring model—concept \n
The concept of TMI integrity monitoring system as a whole has some peculiarities:
Simulated parameters of the aviation engine operation and TMI can be presented in the form of multidimensional technological time series;
Monitoring the TMI integrity is based on the analysis of the consistency of the behavior of parameters obtained by using the model of complex technical object, and taken from the on-board aircraft systems;
The output of the monitoring system is the evaluation of conditional probability of the events of data integrity violation events.
Risk value estimate due to violation of TMI information integrity after applying the tool based on the integrity monitoring model is \n
Due to the significant amount of computation when working with FGCM containing a large number of concepts, it was necessary to develop a software tool to automate cognitive modeling with use of FGCM. The change in the state of concepts over time and the final states of the target concepts of the FGCM, calculated in the developed software tool, are presented in Figure 9.
\nTo improve an efficiency of risk analysis and management with use of FGCM, the special software tool “Cognitive Map Constructor” was developed. This software allows us to build and edit FGCM, use them to carry out the security risk analysis, and justify the choice of the necessary countermeasures from the given user-specified set. As a result, a diagram of risk assessment is built under various scenarios of countermeasures’ implementation and threats’ realization.
\nBesides supporting the FGCM with the installation of connections weights in the form of the upper and lower boundaries, the software allows us the use of linguistic terms of fuzzy logic, as well as setting the weights in the form of “white” crisp numbers. The software has the interface implemented in HTML using CSS, which allows displaying the FGCM and all the necessary accompanying information by the concepts and connections, and also is able to work on any graphical operating system that has a current Web browser.
\nThere are five kinds of concepts which are used in FGCM: threats, information assets, intermediate concepts, targets, and countermeasures, which can be marked by different colors for convenience and clarity.
\nThe set of the options depends on the type of the concept, but in most cases its name is specified with description, as well as its current state. In the case when the weights of all connections, pointing to the concept, are assumed to be equal, one can mark the option “Imposed weight” and set the desired value. For countermeasures, it is permissible to indicate which of existing countermeasure it is, that allows realizing situations when one countermeasure acts on several connections at once.
\nTo establish the relationships between the concepts, it is necessary to click on the button “Placement” of the action group “Connections” in the tool window. After that, the connections are located by pressing consecutively on the initial and final element. The located countermeasures and initial states of the concepts can be adjusted and combined, creating the different scenarios that allow us to compare the effectiveness of countermeasures.
\n\nFigures 10 and 11 show the FGCM risk estimates built in the “Cognitive Map Constructor.”
\nFGCM for risk assessment of data collection and storage subsystem at the service stations (Zone 1) (software window form).
FGCM for risk assessment in the core of the CIN (Zone 2) and TMI (software window form).
Thus, the developed software “Cognitive Map Constructor” allows evaluating the effectiveness of the use of the TMI integrity monitoring system in the protection of telemetric information from the effects of external and internal threats.
\nA promising way to solve the problem of assessing the cybersecurity risks of industrial automated systems is to model the threats realization scenarios using the tools of topological analysis of the system security and cognitive modeling with the aid of Fuzzy Grey Cognitive Maps.
\nAt the basis of this approach, the construction of original FGCM is proposed to assess the risk of automated control system with the following decomposition of FGCM into the number of cognitive maps of the next level of detail (the same as it is done in IDEF0 Functional Modeling technology). The features of construction of this procedure are discussed in this chapter in relation to the task of ensuring the telemetry information integrity in the industrial automated system for collecting, storing, and processing information on the conditions of on-board aviation systems. It is shown that the use of FGCM allows us to obtain more reliable estimates of security risk factors with account of the possible variations of the available actual data and expert opinions.
\nTo automate the proposed risk assessment procedure in the considered system for collecting, storing, and processing telemetry information with use of FGCM, the software tool “Cognitive Map Constructor” was developed, which can be used for identifying the most dangerous vulnerabilities in the system and evaluating the effectiveness of various measures (countermeasures) realization for telemetric information protection from the impact of external and internal threats.
\nThe reported study was funded by RFBR according to the research Project No. 18-00-00238 “Decision support methods and models for innovative project management based on knowledge engineering.”
\nIntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
",metaTitle:"Retraction and Correction Policy",metaDescription:"Retraction and Correction Policy",metaKeywords:null,canonicalURL:"/page/retraction-and-correction-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\\n\\n1. RETRACTIONS
\\n\\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\\n\\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\\n\\nPublishing of a Retraction Notice will adhere to the following guidelines:
\\n\\n1.2. REMOVALS AND CANCELLATIONS
\\n\\n2. STATEMENTS OF CONCERN
\\n\\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\\n\\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\\n\\n3. CORRECTIONS
\\n\\nA Correction will be issued by the Academic Editor when:
\\n\\n3.1. ERRATUM
\\n\\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
\\n\\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\\n\\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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