Modeling of Operation of Information System for Critical Use in the Conditions of Influence of a Complex Certain Negative Factor

Bisikalo, Oleg; Danylchuk, Oksana; Kovtun, Viacheslav; Kovtun, Oksana; Никитенко, Олена Дмитрівна; Vysotska, Victoria

doi:10.1007/s12555-021-0368-6

Cited by 8 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To achieve the same coverage density, we already need 10 20 points, that is, 10 18 times more points compared to the original 1-dimensional space. This example demonstrates the reason for the inefficiency of the brute force method in typical machine learning problems (classification, clustering, and regression) [5][6][7][8][9]. The paradox is that it is impossible to solve the mentioned applied problems using a small number of parameters and achieve adequate results.…”

Section: Introductionmentioning

confidence: 96%

Small Stochastic Data Compactification Concept Justified in the Entropy Basis

Kovtun,

Zaitseva,

Levashenko

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

Measurement is a typical way of gathering information about an investigated object, generalized by a finite set of characteristic parameters. The result of each iteration of the measurement is an instance of the class of the investigated object in the form of a set of values of characteristic parameters. An ordered set of instances forms a collection whose dimensionality for a real object is a factor that cannot be ignored. Managing the dimensionality of data collections, as well as classification, regression, and clustering, are fundamental problems for machine learning. Compactification is the approximation of the original data collection by an equivalent collection (with a reduced dimension of characteristic parameters) with the control of accompanying information capacity losses. Related to compactification is the data completeness verifying procedure, which is characteristic of the data reliability assessment. If there are stochastic parameters among the initial data collection characteristic parameters, the compactification procedure becomes more complicated. To take this into account, this study proposes a model of a structured collection of stochastic data defined in terms of relative entropy. The compactification of such a data model is formalized by an iterative procedure aimed at maximizing the relative entropy of sequential implementation of direct and reverse projections of data collections, taking into account the estimates of the probability distribution densities of their attributes. The procedure for approximating the relative entropy function of compactification to reduce the computational complexity of the latter is proposed. To qualitatively assess compactification this study undertakes a formal analysis that uses data collection information capacity and the absolute and relative share of information losses due to compaction as its metrics. Taking into account the semantic connection of compactification and completeness, the proposed metric is also relevant for the task of assessing data reliability. Testing the proposed compactification procedure proved both its stability and efficiency in comparison with previously used analogues, such as the principal component analysis method and the random projection method.

show abstract

Section: Introductionmentioning

confidence: 96%

Small Stochastic Data Compactification Concept Justified in the Entropy Basis

Kovtun,

Zaitseva,

Levashenko

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…This circumstance suggests that the author's method may be appropriate for determining pessimistic scenarios when analyzing the reliability of critical systems in conditions of incomplete or distorted telemetry data. This direction can be developed taking into account the fact that the authors previously proposed a mathematical apparatus for describing the influence of complex negative factors on an information system for critical use based on the Markov processes theory [49][50][51] .…”

Section: Discussionmentioning

confidence: 99%

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Kovtun,

Grochla,

Kharchenko

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.

show abstract

“…. To move on to the criteria space, characterized by metricity, let us normalize vector   q zd by limitations vector H and obtain relative criteria vector [5,6]:…”

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

“…The use of this procedure will ensure that dD  , in this way the search of the only optimal solution by the decision-maker will be based on pareto-optimal set [4][5][6]. To ensure the efficiency of the model it is necessary to introduce some rule, according to which the weighting factors will be determined [1,2,5]. The search of optimal solution must be in interactive mode, and developing the search procedure should take into consideration the training of the decisive rule.…”

Section: Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Control algorithm for decision making in multi-criteria optimization problems

Grishko

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The main feature of the multi-criteria optimization of the design processes of complex electronic devices is that they are all interconnected, heterogeneous in nature and, as a rule, non-linear. That is, by changing some of them by varying the corresponding parameters, we inevitably change other parameters of the system, and not always in the right direction. The purpose of the work is to propose a way to organize the procedure for finding the optimal solution to multicriteria design problems. When developing and creating new schemes, structures and systems, the task of determining the optimal values of the parameters of the designed technical device or system is reduced to the task of finding the extremum of a certain function, called the quality function. Usually, it is a non-linear function of many independent variables, which, as a rule, cannot be investigated by analytical methods. In this situation, it is advisable to search for a solution to the problem using special algorithms. At the early stages of designing complex radio electronic devices, an algorithm for finding the optimal solution based on the application of search optimization theory methods is proposed. The paper describes the procedure for finding the optimal solution, the distinctive feature of which is that during the search for a solution, information is accumulated and a search direction vector is constructed. This vector offers options for changing weights for subsequent search procedures. The algorithm developed on its basis is used in the information system for the complex analysis of the designs of radio-electronic facilities. The use of random search provides the algorithm with global properties, reduces the number of objective function calculations by one step, which is especially noticeable with a large number of optimized parameters. Allows you to quite simply include any restrictions on the set of valid values of the vector of optimized parameters, which is not always possible when using regular search methods. The proposed algorithm is simply tuned to search for a global extremum by selecting the appropriate parameters. For each specific case, there are some optimal ratios of the values of these algorithm parameters, which, when searching for a solution, reduce the number of working steps. The developed algorithm is less critical to the optimal values of its own parameters, which makes it much easier to tune it when solving practical problems of optimizing design processes.

show abstract

Modeling of Operation of Information System for Critical Use in the Conditions of Influence of a Complex Certain Negative Factor

Cited by 8 publications

References 34 publications

Small Stochastic Data Compactification Concept Justified in the Entropy Basis

Small Stochastic Data Compactification Concept Justified in the Entropy Basis

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Control algorithm for decision making in multi-criteria optimization problems

Contact Info

Product

Resources

About