ICTERI 2015 Batsakis, S., Mayr, H. C., Yakovyna, V., Nikitchenko, M., Zholtkevych, G., Kharchenko, V., Kravtsov, H., Kobets, V., Peschanenko, V., Ermolayev, V., Bobalo, Yu. and Spivakovsky, A., (Eds.): ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer. Proc. 11th Int.
This volume represents the proceedings of the 11th International Conference on ICT in Education, Research, and Industrial Applications, held in Lviv, Ukraine, in May 2015. It comprises 45 contributed papers that were carefully peer reviewed (3-4 reviews per paper) and selected from 119 submissions.
The volume opens with the abstracts of the keynote talks and tutorial. The rest of the collection is organized in 2 parts. Part I contains the contributions to the main ICTERI conference, structured in four topical sections: (1) Teaching ICT and Using ICT in Education; (2) Model-Based Software System Development; (3) Machine Intelligence, Knowledge Engineering and Management for ICT; and (4) ICT in Industrial Applications. Part II comprises the contributions of the four workshops colocated with ICTERI 2015, namely: the International Workshop on Information Technologies in Economic Research (ITER 2015); the International Workshop on Methods and Resources of Distance Learning (MRDL 2015); the International Workshop on Algebraic, Logical, and Algorithmic Methods of System Modeling, Specification and Verification (SMSV 2015); and the International Workshop on Theory of
ICTERI, the International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications: Integration, Harmonization, and Knowledge Transfer, has become a considerable and stable international ICT conference. It is a real pleasure for all ICTERI players, that in contrast to 2014, the 11th edition could bring scholars and expert representatives physically together again for exchanging and discussing new ideas and findings, and for networking across all political borders. This is all the more pleasing as, despite of all current challenges, the Ukrainian ICT community proves its vigor and global integration.
We gladly present you the proceedings of ICTERI 2015, which was held in Lviv, Ukraine, on May 14-16, 2015. The conference scope was determined by the cornerstones of ICT Infrastructures and Techniques, Knowledge Based Systems, Academia/Industry ICT Cooperation, and ICT in education. Special emphasis was given to real world applications of ICT solutions. Therefore, the contributions had to describe original, not previously published work, and to demonstrate how and to what purpose and extent the proposed solutions are applied or transferred into use.
For the main conference, 42 full papers were submitted and evaluated by at least three peers per paper. Finally, 16 have been selected and accepted after revision in accordance with the reviewers comments. This corresponds to an acceptance rate of 38%. The program was rounded off with the two outstanding keynote talks on Rigorous Semantics and Refinement for Business Processes by Klaus-Dieter Schewe and on Smart Learning Environments: a Shift of Paradigm by David Esteban. The tutorial on Systematic Business Process Modeling in a Nutshell by Heinrich C. Mayr complemented the program, in particular regarding the emphasis on the synergy of education and industrial applications.
ICTERI 2015 continued the tradition of hosting co-located events, this year by offering four workshops: 4th Int. Workshop on Information technologies in economic research (ITER 2015) 3rd Int. Workshop on Methods and Resources for Distance Learning (MRDL 2015) 4th Int. Workshop on Algebraic, Logical, and Algorithmic Methods of System
Int. Workshop on Theory of Reliability for Modern Information Technologies (TheRMIT 2015)
In total, these workshops attracted 77 submissions, from which 29 were selected by the particular program committees. This again led to an acceptance rate of 38%.
Clearly, the conference would not have been possible without the engaged support of many people including the authors, members of our Program Committee, workshop organizers and their program committees, local organizers, and, last but not least, generous donators. We express our special thanks to all of them.
May, 2015
Sotiris Batsakis, Heinrich C. Mayr, Vitaliy Yakovyna, Mykola Nikitchenko, Grygoriy Zholtkevych, Vyacheslav Kharchenko, Hennadiy Kravtsov, Vitaliy Kobets, Vladimir Peschanenko, Vadim Ermolayev, Yuriy Bobalo, Aleksander Spivakovsky
General Chairs Steering Committee
Yuriy Bobalo, Lviv Polytechnic National University, Ukraine Aleksander Spivakovsky, Kherson State University, Ukraine Vadim Ermolayev, Zaporizhzhya National University, Ukraine Aleksander Spivakovsky, Kherson State University, Ukraine
Mikhail Zavileysky, DataArt, Russian Federation Local Organization Chair
Dmytro Fedasyuk, Lviv Polytechnic National University, Ukraine Program Chairs Workshop Chairs Tutorial Chair IT Talks Chairs
Vadim Ermolayev, Zaporizhzhya National University, Ukraine Heinrich C. Mayr, Alpen-Adria-Universät Klagenfurt, Austria Mykola Nikitchenko, Taras Shevchenko National University of Kyiv, Ukraine Aleksander Spivakovsky, Kherson State University, Ukraine Mikhail Zavileysky, DataArt, Russian Federation Grygoriy Zholtkevych, V.N.Karazin Kharkiv National University, Ukraine Sotiris Batsakis, University of Huddersfield, UK Heinrich C. Mayr, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria Vitaliy Yakovyna, Lviv Polytechnic National University, Ukraine Mykola Nikitchenko, Taras Shevchenko National University of Kyiv, Ukraine Grygoriy Zholtkevych, V.N.Karazin Kharkiv National University, Ukraine Publicity Chair Web Chair
Nataliya Kushnir, Kherson State University, Ukraine
Eugene Alferov, Kherson State University, Ukraine Program Committees
MAIN ICTERI 2015 Conference
Carlos Ruiz, playence, Spain Abdel-Badeeh Salem, Ain Shams University, Cairo, Egypt Wolfgang Schreiner, RISC, Johannes Kepler University Linz, Austria Pavlo Serdyuk, Lviv Polytechnic National University, Ukraine Vladimir A. Shekhovtsov, Alpen-Adria-Universität Klagenfurt, Austria Mariya Shishkina, Institute of Information Technologies and Learning Tools of the National Academy of Pedagogical Sciences of Ukraine, Ukraine Martin Strecker, IRIT, Paul Sabatier University, Toulouse, France Ilias Tachmazidis, University of Huddersfield, UK Olga Tatarintseva, Satelliz, Ukraine Vagan Terziyan, University of Jyväskylä, Finland Ville Tirronen, University of Jyvaskyla, Finland Nikolay Tkachuk, National Technical University "Kharkiv Polytechnic Institute”, Ukraine Mauro Vallati, University of Huddersfield, UK Leo Van Moergestel, Utrecht University of Applied Sciences, The Netherlands Maxim Vinnik, Kherson State University, Ukraine Paul Warren, Knowledge Media Institute, the Open University, UK Vitaliy Yakovyna, Lviv Polytechnic National University, Ukraine Yulia Nosenko (Zaporozhchenko), Institute of Information Technologies and Learning Tools of the National Academy of Pedagogical Sciences of Ukraine, Ukraine Iryna Zaretska, V. N. Karazin Kharkiv National University, Ukraine
Kalliopi Kravari, Aristotle University of Thessaloniki, Greece Rustam Gamzaev, National Technical University "Kharkiv Polytechnic Institute”, Ukraine Eleftherios Spyromitros-Xioufis, Aristotle University of Thessaloniki, Greece Emmanouil Rigas, Aristotle University of Thessaloniki, Greece ITER 2015 Workshop
Vitaliy Kobets, Kherson State University, Ukraine Sergey Kryukov, Southern Federal University, Russian Federation Sergey Mazol, Academy of Public Administration, Minsk, Belarus Tatyana Payentko, National University of State Tax Service of Ukraine, Ukraine
Tom Coupe, Kyiv School of Economics, Ukraine Dorota Jelonek, Częstochowa University of Technology, Poland Ludmila Konstants, American University of Central Asia, Kyrgyz Republic Sergey Kryukov, Southern Federal University, Russian Federation Sergey Mazol, Academy of Public Administration, Minsk, Belarus Marin Neykov, University of National and World Economy (UNWE), Bulgaria Nina Solovyova, Kherson State University, Ukraine Ekaterina Vostrikova, Astrakhan State University, Russian Federation Alexander Weissbult, Kherson State University, Ukraine MRDL 2015 Workshop
Vladimir Kukharenko, National Technical University “Kharkiv Polytechnic Institute”, Ukraine Yulia Nosenko (Zaporozhchenko), Institute of Information Technologies and Learning Tools of the National Academy of Pedagogical Sciences of Ukraine, Ukraine Hennadiy Kravtsov, Kherson State University, Ukraine Olga Gnedkova, Kherson State University, Ukraine Alexander Kolgatin, H.S. Skovoroda Kharkiv National Pedagogical University, Ukraine Evgen Kozlovskiy, Kherson State University, Ukraine Vladislav Kruglik, Kherson State University, Ukraine Michael Sherman, Kherson State University, Ukraine Maria Shishkina, Institute of Information Technologies and Learning Tools
of the National Academy of Pedagogical Sciences of Ukraine, Ukraine Tatyana Zaytseva, Kherson State Maritime Academy, Ukraine SMSV 2015 Workshop
Wolfgang Schreiner, RISC, Johannes Kepler University Linz, Austria Mykola Nikitchenko, Taras Shevchenko National University of Kyiv, Ukraine Michael Lvov, Kherson State University, Ukraine Martin Strecker, IRIT, Paul Sabatier University, France
Anatoliy Doroshenko, Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Ukraine Louis Feraud, Paul Sabatier University, France Alexander Letichevsky, Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Ukraine Alexander Lyaletski, Taras Shevchenko National University of Kyiv, Ukraine Frederic Mallet, University of Nice Sophia Antipolis, France Vladimir Peschanenko, Kherson State University, Ukraine TheRMIT 2015 Workshop
Vyacheslav Kharchenko, National Aerospace University “KhAI”, Ukraine Elena Zaitseva, Žilina University, Slovakia Bogdan Volochiy, Lviv Polytechnic National University, Ukraine
Mario Fusani, ISTI-CNR System and Software Evaluation Center, Italy Vladimir Sklyar, National Aerospace University "KhAI", Ukraine Iosif Androulidakis, Ioannina University Network Operations Center, Greece Yuriy Kondratenko, Black Sea State University named after Petro Mohyla, Ukraine Vitaly Levashenko, Žilina University, Slovakia Dmitriy Maevskiy, Odessa National Polytechnic University, Ukraine Vladimir Mokhor, Pukhov Institute for Modeling in Energy Engineering, NASU, Ukraine Oleg Odarushchenko, Research and Production Company Radiy, Ukraine Olexandr Gordieiev, University of Banking of National Bank of Ukraine, Kyiv, Ukraine Yurij Ponochovny, Poltava National Technical University, Ukraine Jüri Vain, Tallinn University of Technology, Estonia Sergiy Vilkomir, East Carolina University, USA
Vladimir Zaslavskiy, Taras Shevchenko National University of Kyiv, Ukraine Local Organizing Committee
http://spivakovsky.fund/) aims to support gifted young people, outstanding educators, and also those who wish to start up their own business. OSEF activity is focused on the support and further development of educational, scientific, cultural, social and intellectual spheres in the Kherson Region of
plications, helping clients optimize time-to-market and minimize software development risks in mission-critical systems.
Lviv Polytechnic National University (http://www.lp.edu.ua/en) is the largest technological university in Lviv. Since its foundation in 1844, it was one of the most important centres of science and technological development in
Logicify (http://logicify.com/) is an outsourcing company providing software development services. Compay helps customers with issues and projects involving software. Logicify has been working in a variety of industries and fields, including telecom, video sharing, social media, insurance. It has several teams with specialized skills in different technologies that can relate to specific industries.
http://dataart.com/ Rigorous Semantics and Refinement for Business Processes . . . . . . . . . . . . .
Klaus-Dieter Schewe Smart Learning Environments: a Shift of Paradigm . . . . . . . . . . . . . . . . . . . .
David Esteban Tutorial Systematic Business Process Modeling in a Nutshell . . . . . . . . . . . . . . . . . . .
Heinrich C. Mayr Part I: Main ICTERI Papers Teaching ICT and Using ICT in Education On the Results of a Study of the Willingness and the Readiness to Use Dynamic Mathematics Software by Future Math Teachers . . . . . . . . . . . . . .
Elena Semenikhina and Marina Drushlyak Model-Based Software System Development Knowledge-Based Approach to Effectiveness Estimation of Post Object-Oriented Technologies in Software Maintenance . . . . . . . . . . . . . . . .
Mykola Tkachuk, Kostiantyn Nagornyi and Rustam Gamzayev Provably Correct Graph Transformations with Small-tALC . . . . . . . . . . . . .
Nadezhda Baklanova, Jon Hael Brenas, Rachid Echahed, Christian Percebois, Martin Strecker and Hanh Nhi Tran A Study of Bi-Objective Models for Decision Support in Software Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vira Liubchenko 1 3 4 5 21 35 51 62 78 94 Method of Evaluating the Success of Software Project Implementation Based on Analysis of Specification Using Neuronet Information Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Tetiana Hovorushchenko and Andriy Krasiy Machine Intelligence, Knowledge Engineering and Management for ICT Calculation Method for a Computer’s Diagnostics of Cardiovascular Diseases Based on Canonical Decompositions of Random Sequences . . . . . 108
Igor P. Atamanyuk and Yuriy P. Kondratenko Synthesis of Time Series Forecasting Scheme Based on Forecasting Models System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Fedir Geche, Vladyslav Kotsovsky, Anatoliy Batyuk, Sandra Geche and Mykhaylo Vashkeba C-Clause Calculi and Refutation Search in First-Order Classical Logic . . . 137
Alexander Lyaletski Principles of Intellectual Control and Classification Optimization in Conditions of Technological Processes of Beneficiation Complexes . . . . . . . 153
Andrey Kupin and Anton Senko ICT in Industrial Applications A Composite Indicator of K-society Measurement . . . . . . . . . . . . . . . . . . . . . 161
Kseniia Ilchenko and Ivan Pyshnograiev Part II: ICTERI Workshop Papers ITER Workshop Papers Risk Assessment of Use of the Dnieper Cascade Hydropower Plants . . . . . 204
Andriy Skrypnyk and Olha Holiachuk Behavioral Aspects of Financial Anomalies in Ukraine . . . . . . . . . . . . . . . . . 214
Tetiana Paientko The Formation of the Deposit Portfolio in Macroeconomic Instability . . . . 225
Andriy Skrypnyk and Maryna Nehrey Dynamic Model of Double Electronic Vickrey Auction . . . . . . . . . . . . . . . . . 236
Vitaliy Kobets, Valeria Yatsenko and Maksim Poltoratskiy The Multidimensional Data Model of Integrated Accounting Needed for Compiling Management Reports Based on Calculation EBITDA Indicator 276
Viktoria Yatsenko MRDL Workshop Papers The Hybrid Service Model of Electronic Resources Access in the Cloud-Based Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
Mariya Shyshkina Methods and Technologies for the Quality Monitoring of Electronic Educational Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
Hennadiy Kravtsov SMSV Workshop Papers Realisation of ”Black Boxes” Using Machines . . . . . . . . . . . . . . . . . . . . . . . . . 326
Grygoriy Zholtkevych An Interleaving Reduction for Reachability Checking in Symbolic Modeling 338 Alexander Letichevsky, Oleksandr Letychevskyi and Vladimir Peschanenko Abstracting an Operational Semantics to Finite Automata . . . . . . . . . . . . . 354 Nadezhda Baklanova, Wilmer Ricciotti, Jan-Georg Smaus and Martin Strecker The Static Analysis of Linear Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366
Michael Lvov and Yulia Tarasich Defining Finitely Supported Mathematics over Sets with Atoms . . . . . . . . . 382
Andrei Alexandru and Gabriel Ciobanu On a Strong Notion of Viability for Switched Systems . . . . . . . . . . . . . . . . . 396
Ievgen Ivanov Natural Computing Modelling of the Polynomial Space Turing Machines . 408
Bogdan Aman and Gabriel Ciobanu TheRMIT Workshop Papers Rigorous Semantics and Refinement for Business ⋆ Processes (Abstract)
Klaus-Dieter Schewe1,2
1 Software Competence Center Hagenberg, Austria, kd.schewe@scch.at
2 Johannes-Kepler-University Linz, Austria, kd.schewe@cdcc.faw.jku.at
ICTERI Key Terms. Mathematical Model, Methodology, Formal Method,
Process, Integration
For the modelling of business processes it is necessary to integrate models for
control flow, messaging, event handling, interaction, data management, and
exception handling. In principle, all common business process models such as BPMN
[
The first concern regarding rigorous semantics has been discussed in detail
by B¨orger in [
The second concern can be addressed by means of horizontal refinement.
On grounds of ASMs necessary subtle distinctions and extensions to the control
flow model such as counters, priorities, freezing, etc. can be easily integrated in
a smooth way [
In the talk a glimpse of the rigorous, ASM-based semantics for business processes is presented. The focus is on the control flow with specific emphasis on priority handling. This is followed by a discussion of horizontal refinement focusing on the introduction of disruptive events and associated exception handling. A simplified example capturing the effects of external change to a running process is used for illustration. Smart Learning Environments: a Shift of Paradigm
David Esteban1 1 TECHFORCE, Vía Augusta, 2bis planta 5ª E-08006 Barcelona, Spain Abstract. The incorporation of Information and Communication Technologies (ICT) as a supporting mechanism in educational processes has already been proved as an important driver in reinforcing both teaching and learning. The extensive development of Learning Management Systems (LMS), software platforms aimed at supporting and articulating e-learning, education courses and training programs, is already backed by a relevant ICT industry, with significant market penetration. The emergence of the new concept of Smart Learning Environments (SLEs) is shifting the main focus of LMSs on courseware towards a more efficient and effective approach focused on teaching and learning processes, thus in the students themselves and in the teachers as key players. The evolving concept of SLEs encompasses blending educational technologies with appropriate considerations and guidance developed by pedagogical and educational neuroscience domains, thus opening up room for interesting scientific and technological challenges. Systematic Business Process Modeling in a Nutshell
Heinrich C. Mayr1 1Alpen-Adria-Universität Klagenfurt, Universitätsstrasse 65-67 Klagenfurt, 9020, Austria
Heinrich.Mayr@aau.at Abstract. In-depth business process management is crucial for any institution and enterprise in a competitive world. Although this insight is by no means new, the daily practice draws another picture: Certainly, many enterprises have defined their overall strategy including IT issues at least roughly, and, based here on, have documented their business processes somehow. Rarely however, do they manage their business processes comprehensively in the sense of covering analysis, design, measurement, continuous optimization, and IT support.
The key prerequisite for allowing such comprehensive handling of business processes is to describe these processes transparently and completely, using a modeling language that is appropriate for the particular context including all stakeholders concerned.
The aims of this tutorial, therefore, are threefold: (1) the participants will learn about the fundamentals of business processes and their contexts; (2) the key features of popular business process modeling languages like Adonis and BPMN; and (3) guidelines for selecting an appropriate modeling approach including the customization to the given environment.
Intended audience: Practitioners and researchers who are interested in a systematic approach to business process management, and have basic knowledge in modeling and information systems engineering.
Keywords. Business process fundamental, business process context, business process modeling language, selection of the modeling approach, customization Key Terms. Process, ProcessPattern, Technology, Methodology, Model Using ICT in Training Scientific Personnel in Ukraine:
Status and Perspectives
Aleksandr Spivakovsky, Maksim Vinnik and Yulia Tarasich Kherson State University, 27, 40 rokiv Zhovtnya St., 73000 Kherson, Ukraine {Spivakovsky, Vinnik, YuTarasich}@kspu.edu Abstract. Today an enormous amount of problems in building a system of efficient education and science is on the discussion agenda in Ukraine. A decrease in the number of scientists in the country has been observed in the last 15 years. At the same time, the amount of postgraduate students and people aiming at obtaining their doctorate is increasing. Notably, similar indicators are also observed in the majority of post-soviet countries. One complicating factor is that the system of scientific personnel training in Ukraine is very restrictive and closed. The proportion of research results published using a free access scheme to the overall bulk of publications is still very small, in particular if compared to the level of ICT development. Therefore, a major part of the publications still remains inaccessible from the outside. In this study we investigate the openness and accessibility of the preparation of the academic staff in Ukraine. As a result we come up with a proposal of requirements to the ICT infrastructure in this area.
Key Terms: ICT Infrastructure, Research.
''If it's not on the Web, it doesn't exist at all''
Sarah Stevens-Rayburn & Ellen N. Bouton, 1997 1 Introduction The main catalyst for socio-economic development of a state potential is the ability to create, collect, and effectively manage knowledge that is comes out from the best scholarly research practices. The countries which have made it to their development strategy and implemented the effective interaction with the business enjoy TOP ratings in the World rankings. In the age of information technologies, it takes one not years, but rather days to bear the bell of scientific research and excel the competitors. The companies which are the first in the market are more likely to benefit from a positive effect caused by the introduction of new knowledge. Globalization is adjusting the cooperation between science and industry. More and more funds are invested in scientific research and development to capture the leadership in the market. A modern country's development is stimulated by the transition from a resource-based economy to hi-tech. There is an opportunity to create “intellectual dollars” without any resource, but people. The results of intellectual work become a hard currency. For example, Japan, though it had no natural resources, managed to become the leader in world's economy. The monetary value of the biggest hi-tech (IT) companies is at a scale of the budgets of some developed countries (Apple – $ 711 billion, Microsoft – $ 349 billion, Google – $ 365 billion).
The Open Science (OS) movement gains popularity in the world of clerisy, aiming
to make research results and source data accessible to public at all levels. However,
there is a conflict between the desire of scientists to have access to shared resources
and make profit by using these resources [
Today, a lot of research in Ukraine is devoted to the problems of higher education and, in particular, the use of ICT for training students, creating information and communication environments in the universities, etc. However, in the scholarly literature insufficient attention is paid to the development of information and communication models of interaction with ІCT in academic staff training. Moreover, today we are talking about the need for openness and accessibility of scientific activity, whereas a substantial part of the scholarly output never reaches its reader within and even more outside the professional academic community. This problem is particularly acute in the post-soviet countries. Regionalism of entire areas in science, convention, low connection with contemporary scientific trends, low level of foreign language knowledge by scientists, lack of self-developing scientific community, low competition with other countries, lack of motivation, poor funding, brain drain, and a number of other factors result in the continuing archaism of scientific brainpower training in Ukraine.
Scientometrics is rapidly developing nowadays. Using information technology allows creating new services for the development of scientific and research activity. Many global companies invest billions of dollars in services to support research activity, thereby creating a serious market not for the research results but for the research process support. Herewith the trend shifts toward commercial projects. The examples of such companies are Apple, Microsoft, Google, Elsevier, Thomson Reuters, not to mention many others. The most outstanding services with rapidly growing impact are Google Scholar, Scopus, Orcid, Academia.edu, Research Gate, Mendeley, arXiv.org, cs2n, Epernicus, Myexperiment, Network.nature, Sciencecommunity. These services contribute to satisfying the needs of the scientific community. In fact, these positively influence scientific and technical progress and create a new paradigm of scientific research. A big number of the recently created scientometric services allow assessing the relevance of the research results by a scientist, the number of his publications, citations, storage, etc. Having these measurements at hand opens up new opportunities and prospects. Our time is characterized by the high rates of the accumulation of new knowledge, in particular in А С
E B
D Fig. 3. The RBD of the fault-tolerant system On the basis of developed binary SAM of the fault-tolerant system, which consists of set of formal parameters (Fig. 4), SV components and failure condition (Fig. 5), the tree of modification rules of state vector (Fig. 6), which is the input to the software module ASNA, the graph states and transitions was obtained in the automatic mode (Fig. 7). Fig .5. State vector components and failure condition
Basing on the obtained graph of states and transitions the software module ASNA formed mathematical model of the system as a system of Chapmen - Kolmogorov differential equations. After its solving the probability of being in every possible state was obtained. Probability of system being in operable state is 0.9894, and the probability of failure is equal to:
Qf = 1 - 0,9894 = 0,01061 λA
ABC
DE λE λD λB λC
λD BCDE λE
λA ACD λC
λE λA ABD
λE λD λB ABCE λC λE λC λB λB λA λB λA ABC λC λD
CDE λD λC
BDE ADE λE λB λA λD λA ABE λB λE ABD λD DE λD
λE λA
BCD λB ACD λA λD λB
BCE ACE λC λB λA λC λD λE CE
BE AE λC λB λA CD λC λE
E λE
SF
On the basis of the graph of states and transitions according to developed algorithm,
it was determined that after simultaneous failure of modules E and D the system fails
in general. Next, other two combinations which also lead to failure of the whole system
are ACE and BCE. Thus, these three combinations make the MCS. The next stage was
the determining of the values of the probability of each of these combinations.
Substituting logical expression of MCS DE: ((V4 = 0) AND (V5 = 0)) instead of failure
condition the MCS value of probability simultaneous failure of combination of modules
E and D was obtained, which is QDE = 0,009. Similarly, substituting logical expression
of MCS ACE and BCE instead of failure condition we get: QACE = 0,00084; and QBCE
= 0.00084. The calculated MCS is shown in Table. 1.
Validation of the
developed method. To
validate the developed
method it was
implemented the a
fault tree building for
the system (Fig. 8)
according to the
approach [
The validation was performed using specialized software suite RAM Commander by ALD Service. For RBD the fault tree was set up (Fig. 8) and MCS were obtained by tools of RAM Commander and are shown in Fig. 9.
The comparison shows that the calculated values of MCS, which were obtained from fault tree using software suite RAM Commander coincide with the values obtained from the graph of states and transitions with the split failure state using binary SAM. The developed approach (Fig. 2) allows us to get the MCS in automatic mode without fault tree construction.
Expanding the Functionality of the Program ASNA for the
Safety Analysis of CTSCA
For building complex models, which are focused on determination of the reliability and safety indexes it is most advisable to take as a basis the graph of states and transitions with split state of catastrophic failure and method for its automated construction using binary SAM. However, the biggest problem for the designer, in this case, is the construction of the binary SAM because its formation requires from the developer not only the deep knowledge of the nuances of functionality of designed CTSCA but also thorough knowledge about techniques of construction the formalized graph of states and transitions that is the whole direction in complex systems designing.
Therefore, the next urgent task is to automate the construction of binary SAM-based
graphical representation of the system as a RBD. This will speed up the development
of SAM, reduce the time cost in degree and obtain both reliability and safety indexes.
Principles of this automation were laid in works [
According to approach [
In order to extend the functionality of the ASNA software for safety analysis of
CTSCA it is needed to combine binary SAM methodology with the approach [
The initial value of each component is equal to one: Vi1=1; Type of connection of RBD elements (serial, parallel, combined) is given by the inoperable condition If the limited number of renewals of system is planned, for each item is added another SV component – counter of repairs:
→ V12, V22, … , Vi2, …
The initial value of each component is equal to zero: Vi2=0 If the number of renewals is unlimited, the additional component isn’t added; Each RBD element is assigned to line of binary SAM as follows:
Event Condition FCIT FCPAT MRSV Failure of module і Vi1=1 i 1 Vi1=0 If the system is renewable, in addition to each RBD element, another line is assigned to binary SAM as follows:
Event Condition FCIT FCPAT MRSV Repair of module і (Vi1=0) AND µi 1 Vi1=1 (Vi2<RCi) Vi2= Vi2+1 Parametres of each element (failure rate - i, the intensity of repair - i, the number of repairs - RCi etc.) is transmitted to set of formal parametres; Limited values of each RBD element repair, the number of repair crews, repair priority are transmitted to set of formal parametres; Inoperable conditions are transmitted to SAM and serves to filter the operablebodied and inoperable states.
Thus all components of SAM can be automatically formed. Generated data can be
represented as a file that is sent to ASNA software module as input data. ASNA
software module enables automated obtaining of the graph of states and transitions with
split failure state. Basing on the graph of states and transitions ASNA software makes
it possible to assess reliability. CutSetDefiner software, basing on the graph of states
and transitions, can generate MCS and basing on MCS through software [
Conclusions 1. Split of critical failure state in graph of states and transitions, in contrast to the known approaches, allows estimation of reliability and safety indexes, that makes the impact of maintenance strategies on safety and reliability, impact of the fault tolerance on safety to be considered. This will increase the accuracy (certainty) of efficiency indexes estimation of complex technical systems for critical application. 2. Minimal cut sets obtaining on the basis of the graph of states and transitions allows taking into account the interrelations of accidents directly from the analysis of system states for identification weaknesses. It gives only reasonable means for providing fault tolerance that reasonably reduces the cost of improving the system. 3. Using binary structural-automatic model allows automated obtaining of split critical failure state and reducing time costs for building the graph of states and transitions. 4. Risk reduction factor was introduced for quantitatively assess of the efficiency of improving safety by improving reliability by introducing redundancy in critical elements of complex technical systems for critical application. 5. Fault tree building from the graph of states and transitions basing on minimal cut sets takes into account the behavior of complex system that is not available when using static and dynamic fault trees 6. The combination of binary structural-automatic model and method of automated constructing of graph of states and transitions basing on reliability block diagram makes it possible to automate the procedure of building structural-automatic model of fault-tolerant renewable complex technical systems for critical application and reduce time costs by more than degree. Scenario-Based Markovian Modeling of Web-System Availability Considering Attacks on Vulnerabilities Vyacheslav Kharchenko1, Yurij Ponochovny2, Artem Boyarchuk1 and Anatoliy
Gorbenko1 1 National Aerospace University KhAI, Kharkiv, Ukraine
V.Kharchenko@khai.edu 2 Poltava National Technical University named after Yurij Kondratyuk, Poltava, Ukraine pnch1@rambler.ru Abstract. In the paper we simulate web-system availability taking into account security aspects and different maintenance scenarios. As a case study we have developed two Markov’s models. These models simulate availability of a multitier web-system considering attacks on DNS vulnerabilities in additional to system failures due to hardware/software (HW/SW) faults. Proposed Markov’s model use attacks rate and criticality as initial simulation parameters. In the paper we demonstrate how to estimate these parameters using open vulnerability databases (e.g. National Vulnerability Database). We also define different vulnerability elimination (VE) scenarios and examine how they affect system availability.
Keywords: web-system availability, security, vulnerability, Markov’s models, scenario of vulnerability elimination
Key terms. MathematicalModeling, MathematicalModel, SoftwareSystems 1 Introduction
Efficient implementation and operation of multitier web-systems using COTS
components depend on accuracy of security assessment and quality of attacks
prevention and recovery activities. Security of web-system can be estimated by
analyzing web-components vulnerabilities and predicting attacks affecting system
availability and other security attributes. System availability and accessibility of the
provided ser-vices depend on the used maintenance strategy. This strategy can
implement various vulnerability prevention and elimination scenarios [
To estimate system availability and security researchers develop various simulation
models [
In the paper we analyze web-system availability considering failures caused by
HW/SW faults as well as attacks on system vulnerabilities. With this purpose we
propose and examine a set of Markov’s availability models implementing different
scenarios of vulnerability elimination. This paper continues research described in [
The rest of the paper is organized as follows. In the second section we suggest a set of scenarios to assess web-system availability taking into account different vulnerability elimination procedures. In the third section we discuss a technique of estimating input parameters of Markov’s models by use of information about software component vulnerabilities from the open vulnerability databases. The forth section presents a case study and the set of Markov’s models and also examines simulation results. 2 The Scenario-Based Modeling with Regards Elimination
Approach to to System
Web-System Vulnerabilities
Availability and their
Attacks on vulnerabilities of web-systems can be simulated using Markov’s models
[
In the Fig. 1 we propose a set of common state-transitional models capturing different attack and recovery scenarios. The scenarios are differed by a number of attacked vulnerabilities: one (a-f) or several (g); with (b-g) or without (a) vulnerability elimination; with vulnerability elimination after system been successfully attacked (b-d) or during (e,f) preventive maintenance actions.
We have marked model states as following: double circles correspond to up-states, single line marked circles correspond to maintenance states, thick line marked circles correspond to down-states after attacks.
The simplest scenario is shown in Fig. 1,а. After successful attack a web-system is recovered (e.g. rebooted) without vulnerability elimination. However not all attacks can be successful and lead to web system unavailability. This is why we consider two transitions from up-state S0: the first transition with the rate attack*Da leads to down (unavailable)-state Sd; the second one with the rate attack*(1-Da) returns back to upstate S0 (Da is a probability of attack to be successful).
The second scenario (Fig. 1,b) illustrates vulnerability elimination during system recovery after successful attack. We assume that during recovery action it is possible to eliminate from 0 to all (nv) vulnerabilities. Hence, web-system may return from the down-state Sd to the initial state S0 without vulnerability elimination with the rate ′ a*(1-Dp), where Dp is a probability of successful recovery and vulnerability elimination, or may transit to the next up-state Su with the rate ′ a*Dp. ilty tuho lavo irae
b iw rem lvun t s te i i l i b a r e n l u v l a v o m e r e h t h t i W e v i t n e v e r p h t i W l a r e v e s on se s i k it ttcaa ilrab ith len W vu
S0
nv S0 S0 S0 S0 Sp S0 Sp
S0 Sd Sd Sd
Sd Sd Sd Sd Sd Sd Sd
nv-1 Su Su Su Su Sp Su
Sp Su Sd Sd Sd
Sd Sd
1 Su Su Su Su Sp Su
Sp Sd
Su
Sd Sd Sd Sd Sd Sd 0 Su Su Su Su Su Su a) b) c) Fig. 1. Graph models of scenarios of web-system availability considering different options of vulnerability elimination
The third scenario (Fig. 1,c) describes graduate vulnerability elimination only after successful attacks on these vulnerabilities. In this scenario the total number of vulnerabilities in the system may be unlimited nv → ∞.
The step by step vulnerability elimination is described by the next scenario (Fig. 1,d). In this case it is assumed that restart of web-system is possible without elimination of vulnerability which was attacked. 3 Estimation of Input Availability Models 3.1
Parameters for
Markov’s
Web-System
According with the fifth scenario (Fig. 1,e) vulnerabilities can be detected and eliminated from the system only during the periodic maintenance actions (i.e. security audits) only. After the successful attack a web-system is restarted or reboot without vulnerability elimination. Vulnerabilities can be detected and eliminated from the system only during periodic security audits. The probability of eliminating the i-th vulnerability is equal to αi, Σαi = 1.
The sixth scenario (Fig. 1,f) assumes that vulnerabilities can be detected and eliminated from the system both after successful attacks or during periodic security audits. The seventh scenario takes into account possibility of attacks on several vulnerabilities (Fig. 1,g). The scenario describes sequential chains of attacks on sever-al (four, in our example) services of a web-system. In this case an intruder continues to attack the next services. After successful attack a web-system can transit to a new upstate where vulnerabilities are eliminated from the system or can return back to the initial state by system restarting or rebooting.
Described set of scenarios is not complete. This set includes some basic scenarios. However, other scenarios can be developed considering different procedures of maintenance and vulnerability elimination or patching.
In this section we discuss how parameters of Markov’s models simulating websystem availability can be estimated using existing vulnerability databases like NVD.
The whole set of vulnerabilities stored in NVD can be downloaded as an XML file
«NVD/CVE XML Feed with CVSS and CPE mappings (version 1.2)» [
- Availability impact, A, which can be equal one of three fuzzy values “None” (N), “Partial” (P) and “Complete” (C); - Vector of access, value “Network” (N).
For example, Table 1 presents a subset of vulnerabilities detected during 2013 and causing unavailability of DNS (CVSS_vector – contains – AVμN, AμC и AμP; ns1:descript – contains – DNS (an example for analysis attacks on DNS) including their publishing dates and score. 3.2
Estimation of Attack Rates
In order to parameterizes state-transition models we need to evaluate a rate of the attacks exploiting system vulnerabilities.
This rate obviously depends on different factors including number of system vulnerabilities, their criticality, availability impact and vector of access. However, vulnerabilities define only the capability of a system to be attacked. On the other hand, unlike random system failures, vulnerabilities are exploited by various intended (hacker, computer criminals, industrial espionage, insiders, etc.) and unintended (viruses, worms, malware, etc.) threat agents.
name CVE-2013-0198 CVE-2013-2266 CVE-2013-2494 CVE-2013-1152 CVE-2013-2052 CVE-2013-2053 CVE-2013-2054 CVE-2013-4854 CVE-2013-4115 CVE-2013-5479 CVE-2013-5480
Motivation of intended threat agents is also depended on the system itself (its value and interest for the attacker). Last two factors are really difficult to define quantitatively. Thus, in the paper we propose to define the attack rate by the average per year frequency of vulnerability disclosure in the system components.
Criticality of attack is determined as an average value of basic CVSS estimation. We propose the following technique to estimate attack rate:
1) development of availability block diagram (ABD) of web-systems as a sequentially-parallel connection of components influencing on accessibility (similar to RBD); 2) extraction from NVD the vulnerability subsets for all components of ABD; 3) calculation of average per year frequency of vulnerability disclosure in these subsets;
4) determination of attack rate as the maximum of these frequencies of vulnerability disclosure;
5) calculation of attack criticality as an average value of basic CVSS estimation for selected set per year.
According with Table 1, average attack rate on DNS vulnerabilities causing unavailability could be estimated in 2013 as 1,26*10–3 1/h while the average criticality equals 6,75.
Route DNS
DHCP
Route mudns
ladns 4 Web-System Availability Elimination Scenarios
Models for Different Vulnerability 4.1 Initial Model and its Parameters
Let us examine a web-system based on three network services: DNS, DHCP and Routing. Reliability block diagram (RBD) and Markov’s model (the marked Markov’s chain) of the web-system are shown in Fig. 2.
Fig. 2. Reliability block diagram and Markov’s model of the web-system without considering system vulnerabilities
The RBD consists of three consequently connected components and failure of any
components causes failure (unavailability) of the system. In this section we study two
availability models taking into account attacks on DNS vulnerabilities and different
maintenance operations including security audits [
Initial values of model parameters are presented in Table 2. The models itself have been implemented as Matlab programs. 4.2
The Model MA-1
This model describes a web-system with attacks on DNS vulnerabilities and periodic maintenance activities (security audits) including detection and elimination of vulnerabilities without complication of code (ladns =const). mudns
ladns
Sn+1 j αj
q*muprof mureboot
S10 S11 nv–1 qnv–2*p
nv 1-∑αj S1 S2
S3 mudns
ladns mudhcp
ladhcp muroute laroute
Fig. 3. Marked Markov’s graph for МА-1
Marked Markov’s graph is shown on Fig. 3. As during these activities it is possible to detect and eliminate more than one vulnerability [1…nv], we use a special parameter αj which defines probability of detection of j-th (j [1…nv]) vulnerabilities. Apparently, Σαj = 1, and values α1, α2,… αj,… αnv are distributes on discreet law. For calculation of geometrical distribution law αj was used with parametersμ р=α1=0.7 (probability of detection of the single vulnerability) and q=1–р=0.3 (Table 3).
Initially (state S0) web-system works considering failures and recovering of DNS, DHCP и Routing services (states S1- S3). After attack on DNS (transition to state S5 with the rate d1dns*laatdns) the system fails and can be recovered by restart without vulnerability elimination with rate mureboot. Periodically maintenance activities are performed (state S4) during which 0, 1,…nv vulnerabilities can be eliminated (transitions from state S4 to states S0, S4… Sn). These transitions are weighted using parameter αj*muprof. Further process is continued in the same way (states Sn…Sn+3). 1 0.998 0.996 t)0.994 ( A 0.992 1 )(t0.96 A0.95 3.5
4.5 2.5 t, hours
5 x 104 Fig. 4. Diagram of dependency of availability function for the model МА-1 on different probabilities α1 mureboot =0.05 mureboot =0.1 mureboot =0.5 mureboot =1 mureboot =2
2.5 t, hours 3.5 4.5
5 x 104 Fig. 5. Diagram of dependency of availability function for the model МА-1 on different recovery rate after attack on vulnerabilities, mureboot
The research results of availability function depending on parameters р=α1 and mureboot are shown on Fig. 4 and Fig. 5.
The greater value α1 causes more fast transition of the function A(t) to stationary state (Fig. 4). A value of mureboot influences on a value of availability function minimum, location of minimum on the time axis and time of transition to stationary state (Fig. 5). If mureboot=2 (1/hour) availability function minimum equals 0,9953 for t=17 hours; if mureboot=0.05 (1/hour) availability function minimum equals 0,9103 for t=119 hours.
The model МА-2
This model describes scenarios whish in addition to MA-1 assumes detection and elimination of vulnerabilities both during security audit and right after attack (without complication of code (ladns =const). Marked Markov’s graph is shown on Fig. 6. S0 S1 S2
S3 murecovery* *(1-d2p) murecovery* *d2p q*muprof
S10
an-1*muprof murecovery* *(1-d2p)
murecovery*
S11 *d2p d1dns*laatdns S6 S7 S8
S9
After attack on DNS and transition to state S5 with rate d1dns*laatdns system fails and can be recovered by restart without eliminating vulnerability with the rate (1– d2p)*murecovery or with elimination with the rate d2p*murecovery.
The results of availability function analysis depending on parameters d2p and laprof are shown on Fig. 7 and Fig. 8. The increasing of probability of vulnerability elimination d2 during maintenance activities causes more fast transition of the function A(t) to stationary state (Fig. 8). Changing the availability function depending on the rate of maintenance laprof is dual. On the one hand the rare maintenance activities are carried on the more minimum of availability function on non-stationary phase. On the other side the more often maintenance activities are carried on the faster the function transits to stationary state (Fig. 8). d2p =0 d2p =0.1 d2p =0.2 d2p =0.5 d2p =0.7 d2p =1 laprof =4.57e-2 laprof =4.57e-3 laprof =4.57e-4 laprof =4.57e-5 laprof =4.57e-6 1 0.998 0.996 0.994 )0.992 (t A 0.99 0.988 0.986 0.984
0 1 0.99 0.98 0.97 ) (tA0.96 0.95 0.94 0.93 t, hours
2.5 x 104 Fig. 7. Diagram of dependency of availability function for the model МА-2 on different probabilities of vulnerability elimination after attack d2p Fig. 8. Diagram of dependency of availability function for the model МА-2 on rate of maintenance laprof 4.4
Combining of the Models МА-1 and МА-2
The scenarios corresponding to the models MA-1 and MA-2 can be superposed to increase availability due to increasing of minimum and duration of system transition to the stationary state of availability function. To combine these two scenarios we have developed a set of Matlab programs. Filing of coefficient matrixes was done according with the same initial data (Table3). To solve systems of KolmogorovChapman’s differential equations the method ode15s for time span [0…20000] hours. The results of solving are shown on the Fig. 9. МA-2 model without attacks МA-1 combination of scenarios 1 0.998 0.996 0.994 0.992 ) t (A0.99 0.988 0.986 0.984
2 x 104
Fig. 9. Combining of the models MA-1 and MA-2 (solid line)
According to Fig. 9, the vulnerability elimination scenario MA-1 is better to use till tswitch =750 hours, after this time the scenario MA-2 ensures better availability. Hence at the beginning recovering a system after attack (without vulnerability elimination) is preferable. Then, taking into account increase of the number of failures caused by attacks other scenario (when vulnerabilities are detected and eliminated both after attacks and during maintenance) becomes preferable. It allows increasing the value of availability from 0.984 (MA-2) to 0.988 (MA-1) and decreasing time transition to stationary state from 20000 (MA-1) to 3000 (MA-2) hours. 5 Conclusions
We analyzed a set of web-system behavior scenarios in conditions of attacks on component vulnerabilities. Quantitative assessment and research of availability for such systems can be based on Markov’s models using statistic data about vulnerabilities contained in open databases and described sequence of evaluating of attacks rates and criticality.
We proposed and discussed two models of web-system availability considering attacks on DNS vulnerabilities and different scenarios of vulnerability elimination. There is possibility and reasonability of scenario changing taking into account values of availability function allowing increase minimum one at the non-stationary stage and decrease time of transition to stationary state. This approach allows selecting VE scenario to improve resilience of web-system.
The future research efforts may be concentrated on development of integrated strategies for maintenance and security policies selection taking into account physical, design and interaction faults, and implementation of dynamically reconfigurable weband cloud-systems with embedded monitor and solver to select the optimal strategy of maintenance.
Besides, other types of the vulnerabilities for confidentiality and integrity issues and more detailed model taking into account routing processes can be researched. References
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