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  7. 2022 год
  8. 44-5

Electronic modeling

Vol 44, No 5 (2022)

CONTENTS

Mathematical modeling and Computation Methods

 
HILGURT S.Ya.
A Method of Accelerated Quantitative Evaluation of Components of FPGA-based Security Systems
 
3-24
 
LYSENKOV E.A.
Modeling of Percolation Behavior of Thermal Conductivity in Polymer Nanocomposites Containing Carbon Nanotubes
25-35
 
VEPRYTSKA O.Y., KHARCHENKO V.S.
Analysis of Requirements and Quality Modeloriented Assessment of the Explainable Ai As A Service
 36-50

Computational Processes and Systems

 
KRYVORUCHKO I.P.
Piecewise Linear Approximation of Smooth, Flat Curves by the Secant Method
51-60

Application of Modeling Methods and Facilities

 
KOMAROV M.Yu., HONCHAR S.F., ONYSKOVA A.V., BAKALYNSKYI O.O., PAKHOLCHENKO D.V.
Recommendations for Ensuring Cyber Protection of Industrial Control Systemsof Energy Sector
61-72
 
ZHABOKRYTSKYI I.V.
Current State and Prospects of Increasing the Functionality of Augmented Reality Using Neural Networks
73-89
 
LUKASHEVYCH Ya.P., EVDOKIMOV V.A.
Monitoring Information System of Dynamics of Alternative Electricity Development
90-101
 
POVOROZNYUK O.S., TOPALOV A.M.
Simulation of Planar Thermoelectric Modules with Different Geometrical Shapes Semiconductor Material
102-113

A METHOD OF ACCELERATED QUANTITATIVE EVALUATION OF COMPONENTS OF FPGA-BASED SECURITY SYSTEMS

S.Ya. Hilgurt

Èlektron. model. 2022, 44(5):03-24

https://doi.org/10.15407/emodel.44.05.003

ABSTRACT

Recently, various approaches have been successfully used in information security tools to detect harmful activity, including artificial intelligence technologies. But only the signature approach can completely eliminate recognition errors. That is especially important for critical infrastructure objects. One of the main disadvantages of signature tools is the high computational complexity. Therefore, the developers of such systems turn to hardware implementation, primarily on a reconfigurable platform, that is, using FPGAs. The ability to quickly reprogram FPGAs gives reconfigurable security systems unprecedented flexibility and adaptive possibilities. There are many different approaches to the construction of hardware pattern matching circuits (that are parts of signatures). Choosing the optimal technical solution for recognizing a specific set of patterns is a non-trivial task. For a more efficient distribution of patterns between components, it is necessary to solve an optimization task, the objective function of which includes the quantitative technical characteristics of hardware recognition schemes. Finding these values at each step of the algorithm by performing the full digital circuit synthesis procedure by the CAD is an unacceptably slow approach. The method proposed in this study for the accelerated quantitative evaluation of components of reconfigurable signature-based security systems, based on the use of the so-called evaluation functions, allows solving the problem.

KEYWORDS

signature-based security system, NIDS, multi-pattern string matching, FPGA, quantification

REFERENCES

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    https://doi.org/10.1109/SURV.2011.072210.00075
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  6. Evdokimov, V.F., Davydenko, A.N. and Hilgurt, S.Ya. (2018), "Additional stages of the procedure for online reconfiguration of hardware accelerators for information security tasks", Modelyuvannya ta informatsiyni tekhnolohiyi, Vol. 85, pp. 3-11.
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  17. Hilgurt, S.Ya. (2019), "Constructing CAMs Based on Digital Comparators by Reconfigurable Means for Solving Network Security Tasks", Elektronne modelyuvannya, Vol. 41, no. 3, pp. 59-80, available at: https://doi.org/10.15407/emodel.41.03.059
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  19. Hilgurt, S. (2019), "Constructing Deterministic Finite Automata by Reconfigurable Means for Solving Information Security Tasks", Zakhyst informatsiyi, Vol. 21, no. 2, pp. 111-120, available at: https://doi.org/10.18372/2410-7840.21.13768
  20. Hilgurt, S.Ya. (2021), "Comparative Analysis of Approaches to the Building of Reconfigurable Security Tools Components", Problemy informatyzatsiyi ta upravlinnya, Vol. 2, no. 66, pp. 17-26, available at: https://doi.org/10.18372/2073-4751.66.15712.
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MODELING OF PERCOLATION BEHAVIOR OF THERMAL CONDUCTIVITY IN POLYMER NANOCOMPOSITES CONTAINING CARBON NANOTUBES

Е.А. Lysenkov

Èlektron. model. 2022, 44(5):25-35

https://doi.org/10.15407/emodel.44.05.025

ABSTRACT

An overview of the most correct mathematical models for describing the thermal conductivity of polymer-carbon nanotube systems, which characterize percolation behavior, is given. It is shown that the Landauer model, which does not take into account the presence of a percolation transition at low filler concentrations, is in poor agreement with the experiment. The sigmoidal model describes experimental data well, but is purely empirical. Zhang's model turned out to be incorrect for this type of system, as it was designed for a system with a high filler content. The scaling model showed good agreement with experimental data for a system with a low percolation threshold.

KEYWORDS

thermal conductivity, carbon nanotubes, thermal conductivity models, polymer nanocomposites, percolation behavior

REFERENCES

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  12. Lysenkov, Е.А. (2019), “Simulation of thermal conductivity of polymer nanocomposites, using models based on thermal-electrical analogy”, Nanosistemi, Nanomateriali, Nanotehnologii, Vol. 17, no. 4, pp. 761–772.
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ANALYSIS OF REQUIREMENTS AND QUALITY MODEL-ORIENTED ASSESSMENT OF THE EXPLAINABLE AI AS A SERVICE

O.Y. Veprytska, V.S. Kharchenko

Èlektron. model. 2022, 44(5):36-50

https://doi.org/10.15407/emodel.44.05.036

ABSTRACT

Existing artificial intelligence (AI) services provided by cloud providers (Artificial Intelligence as a Service (AIaaS)) and their explainability have been studied. The characteristics and provision of objective evaluation of explainable AI as a service (eXplainable AI as a Service (XAIaaS)) are defined. AIaaS solutions provided by cloud providers Amazon Web Services, Google Cloud Platform and Microsoft Azure were analyzed. Non-functional requirements for XAIaaS evaluation of such systems have been formed. A model has been developed and an example of the quality assessment of an AI system for image detection of weapons has been provided, and an example of its metric assessment has been provided. Directions for further research: parameterization of explainability and its sub-characteristics for services, development of algorithms for determining metrics for evaluating the quality of AI and XAIaaS systems, development of means for ensuring explainability.

KEYWORDS

explainable artificial intelligence, AI as a Service, requirements for artificial intelligence, model of AI quality, metrics

REFERENCES

  1. Elger, P.P. and Shanaghy E.E. (2020), AI As a Service: Serverless Machine Learning with AWS, Manning Publications Company, available at: https://www.manning.com/books/ai-as-a-service.
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PIECEWISE LINEAR APPROXIMATION OF SMOOTH, FLAT CURVES BY THE SECANT METHOD

I.P. Kryvoruchko

Èlektron. model. 2022, 44(5):51-60

https://doi.org/10.15407/emodel.44.05.051

ABSTRACT

The approximation of smooth lines by a piecewise linear function is relevant in many applications, where this representation can significantly simplify the technological process without a significant loss in quality indicators. An improved method of dividing or representing a smooth curve by a piecewise linear line is proposed, which, in comparison with dividing by the "chord" method, reduces the degree of difference between the approximated and approximating curves for the selected number of segments. The essence of the method consists in the successive point-by-point calculation of the deviation between the approximated line, given analytically, and the straight line segment until the condition of equality of this deviation of the specified tolerance value is met. The next step is to correct the point of intersection of these lines by moving it along the ordinate axis by an amount equal to part of the tolerance in the direction determined by the convexity (concavity) of the original curve. The proposed method of piecewise linear approximation is supposed to be used to implement the sinusoidal motion of the carriage with the sensors in the vibro-calibration system.

KEYWORDS

piecewise linear approximation, secant method, chord method.

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