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“Journal of Applied Informatics” is a peer-reviewed science journal with international representation of editorial board and authors, covering a significant part of Russian IT-area. The topics of the publications are connected to the aspects of theory and application of computer modeling and information technologies in various professional areas. The journal is indexed by Russian Science Citation Index on Web of Science platform.

In accordance with the decision of the Higher Attestation Commission of the Ministry of Education and Science of Russian Federation, journal is included in the «List of Leading Peer-Reviewed Scientific Journals and Publications authorized to publish main dissertation results»

Algorithm for identifying threats to information security in distributed multiservice networks of government bodies

The results of studies are presented, the purpose of which was to develop an algorithm for identifying information security threats in distributed multiservice networks that provide information interaction of regional government bodies, as well as their communication with the population of the region. The relevance of the research topic is due to a significant increase in various types of cyber attacks on the computer networks of public authorities and the need to increase the level of security of these networks by intellectualizing methods for combating information security threats. The algorithm is based on the use of machine learning methods to analyze incoming traffic in order to identify events that affect the state of information security of public authorities. The algorithm provides for input traffic preprocessing, as a result of which a set of images (signatures) obtained from Wasm binary files is formed, and then the image classifier is launched. It contains a sequential inclusion of deep neural networks – a convolutional neural network for signature classification and a recurrent network that processes the sequences obtained at the output of the convolutional network. Features of the formation of signatures in the proposed algorithm, as well as sequences at the input to the recurrent network, make it possible to obtain the resulting assessment of information security, taking into account the history of its current state. The output of the recurrent network is aggregated with the result of comparing the actual signatures with those available in the database. The aggregation is performed by the fuzzy inference system of the second type, using the implication according to the Mamdani algorithm, which generates the final assessment of information security threats. Software was developed that implements the proposed algorithm, experiments were carried out on a synthetic data set, which showed the efficiency of the algorithm, confirmed the feasibility of its further improvement.

An approach to the design of a neural network for the formation of an individual trajectory of knowledge testing

The paper discusses the issues of implementing an adaptive testing system based on the use of artificial neural network (INS) modules, which should solve the problem of intelligent choice of the next question, forming an individual testing trajectory. The aim of the work is to increase the accuracy of the INS to form the level of complexity of the next test question for two types of architectures – direct propagation (FNN – Feedforward Neural Network) and recurrent with long-term short-term memory (LSTM – Long-Short Term Memory). The data affecting the quality of training are analyzed, the architectures of the input layer of the direct propagation INS are considered, which have significantly improved the quality of neural networks. To solve the problem of choosing the thematic block of the question, a hybrid module structure is proposed, including the INS itself and a software module for algorithmic processing of the results obtained from the INS. A study of the feasibility of using direct propagation ANNs in comparison with the LSTM architecture was carried out, the input parameters of the network were identified, various architectures and parameters of the ANN training were compared (algorithms for updating weights, loss functions, the number of training epochs, packet sizes). The substantiation of the choice of a direct distribution network in the structure of the hybrid module for selecting a thematic block is given. The above results were obtained using the Keras high-level library, which allows you to quickly start at the initial stages of research and get the first results. Traditionally, learning has taken place over a large number of eras.

Building the mathematical model of the decision support system in the field of pricing for e-commerce

This work is devoted to the study of pricing issues for obtaining maximum profit when selling consumer goods at a constant purchase price. The said goods come in from either manufacturers or warehouses where the retail companies buy the goods in order to sell them directly to the consumers. The dependence of the selling rate per unit of time on the level of the added price in relation to the purchase price of the item is established by the means of sales price variation. The object of the research is the specific case of a linear approximation of said dependence, which is usually actualized in the event of either more elastic or less elastic demand for goods, when they are sold through Internet platforms. The proposed approach to determining prices of all the goods which are being sold for maximizing the total profit from the sales of all consumer goods or maximizing the total revenue throughout the whole period of sales time, based on the search of extremum points of the profit and revenue functions for each item of goods remains valid in the case of more complex approximations by quadratic and cubic functions of demand function. The type of the function of maximum value added revenue and the type of the function of maximum profit can be both found per unit of time depending on the variable level of the added price included into the sales price of the item. The type of maximum revenue function can be found per unit of time depending on the sales price of the item. The extremum points of the found functions are being determined. The theorems have been proved, that the extremum points which are being determined appear to be the maximum points of the researched functions for each item of goods, when the maximum profit or the maximum revenues are reached by selling goods to consumers. All common variables of said functions are found by summing up these functions among the multitude of goods on the interval of the whole sales time. The received data is used for the practical implementation of an effective sales strategy that ensures maximum profits for companies specializing in direct sales to consumers of the purchased goods. An applied methodicalэф approach to the sales of goods which ensures maximum profit from the sales in the field of elastic demand approximated by a linear function and under the condition of a constant purchase price for goods is proposed and theoretically substantiated.

Comparison of mathematical models of the dynamics of electrically charged gas suspensions for various concentrations of the dispersed component

The author: Tukmakov D.
This work is devoted to mathematical modeling of the dynamics of inhomogeneous electrically charged media. A dusty environment - solid particles suspended in a gas – was considered as an inhomogeneous medium. The mathematical model implemented a continuous approach to modeling the dynamics of inhomogeneous media. The complete hydrodynamic system of equations was solved for each component. The system of equations for the dynamics of each component included the equations of mass continuity, momentum components, and the energy conservation equation for the mixture component. Intercomponent interaction took into account momentum exchange and intercomponent heat transfer. The carrier medium was described as a viscous compressible heat-conducting gas. The flow was described as a flow with a two- dimensional geometry. The equations of the mathematical model were supplemented with initial and boundary conditions. The mathematical model took into account the wall viscosity in the channel. The system of equations of the mathematical model was integrated by McCormack's explicit finite-difference method. To obtain a monotonic grid function, a nonlinear scheme for correcting the numerical solution was used. The mathematical model was supplemented by the Poisson equation describing the electric field formed by charged dispersed particles. Poisson's equation was integrated by finite-difference methods on a gas-dynamic grid. Such a choice of the computational grid was necessary to calculate the concentration of particles required both for solving the electric field equation and for calculating the physical fields of the dynamics of inhomogeneous media. The reciprocal motion of a gas suspension caused by the movement of dispersed particles under the action of the Coulomb force was numerically investigated. The values of the surface and mass densities are determined, at which the models of the surface and mass densities of charges in the simulation of such a process are the same. It is revealed that the surface and mass models of charges are identical with respect to the volumetric content.

Data mining in the management of the Russian higher school

For a comprehensive assessment of the management decisions quality, it is necessary to take into account heterogeneous information presented both in numerical form and in natural language expressions. The effective occurs the use of data mining including neural network clustering and fuzzy set theory. The article presents our approach to the use of these methods for evaluating risks and the management decisions quality in Russian higher education on the example of the implementation of the most ambitious Project 5-100 for it. On the example, the expediency of the neural network clustering to assess the possibility of achieving the goals of any such large-scale project has been proved. Clustering the information database used for the analysis, makes it possible to carry out an objective selection of candidate universities-candidates for the right to receive state subsidies, as well as to adjust the composition of the Project participants. Another methods of intellectual analysis – the construction of a complex of fuzzy inference systems, – confirmed the possibility of a quantitative fi evaluating of the project based on the expert verbal estimates of the project. At the same time, the neural network clustering initially illustrated the unattainability of the Project 5-100 goals. The use of a complex of fuzzy inference systems confirmed this statement by the very low quantitative final assessment of the project on the basis of verbal expert opinions.

Development of a secure neural traffic tunneling system with post-performance evaluation

Currently information exchange methods and means of communication development are being done a significant impact on the level of all industrial and economic entities innovation potential, which is also the same for their group formations, such as regional complexes. It is necessary to note high degree of integration and interdependence of all such systems elements and processes closely interconnected by different kind of networks. Among them, it is possible to highlight the interaction between participants of scientific and industrial cluster within the framework of innovative activities, which should provide possibility to transfer and receive various kinds of data, which could be both open and confidential type. At the current stage, there is not many applied tools for ensuring confidentiality in the implementation of these processes. For example, they partially solve the problem of traffic tunnelling systems based on OpenVPN or WireGuard tunnels, and other software solutions provide the potential of an extensible cloud (Nextcloud). However, analysing the functionality of these solutions, it is possible to identify shortcomings that do not allow their implementation in the complex production and economic systems processes of innovative development. Thus, existing traffic tunnelling solutions are not adapted for deployment on a corporate scale with a flexible organisational structure. In solutions based on Nextcloud, the complexity disadvantages of the server configuration and the cost of the primary software configuration are highlighted. To solve the above problems, in article has been proposed an intelligent traffic tunneling system, which is based on using additional means of primary automated OpenVPN connection initialization at neural module expense. A dynamic digital fingerprint distribution system with two-way key exchange was used as an authorization server. The developed software solution was tested and then compared with existing analogues. This experiment may to conclusion that the developed software solution is not inferior in a number of aspects to existing methods, and can subsequently be used to ensure secure information and communication exchange between industrial and economic entities in clusters during innovative processes implementation.

Development of an integrated space-time database, characterizing an effects of climate change

Currently, at the global and regional (national) levels, the expert community, as well as statesmen, have prioritized the task of practical implementation of the Sustainable Development Goals (characterizing the dynamics of a development of various parameters of socio-ecological and economic systems). Achieving these goals requires serious analytical work based on a deep and comprehensive analysis of a processes taking place in various spheres, as well as the formation of an appropriate information platform, including a set of databases that adequately describe changes taking place in various spheres of state activity. However, today, in Russian practice, this tool is practically absent, which significantly complicates the qualitative and quantitative analysis, assessment and forecasting of the processes of adaptation of Russian regions (including the population living in them) to consequences of global climate change. The purpose of this study is to form a database of indicators characterizing process of adaptation of socio-ecological and economic systems of a northern regions of Western Siberia (The Khanty-Mansiysk Autonomous Okrug and Yamalo-Nenets Autonomous Okrug) to global climate change. The projected database should become the basis for creating an open information resource for a wide range of users when they solve analytical and predictive tasks related to a socio-economic assessment of an impact of global climate warming on permafrost. To achieve the objectives of the study, a set of methods was used: theoretical generalization and comparison, formalization, algorithmization, structuring and grouping, economic and statistical methods (including correlation and regression analysis), cartographic method, graphical modeling method. As a result, we have formed a database representing a single, periodically updated repository containing spatio-temporal data sets characterizing processes and phenomena, on the one hand, socio-ecological and economic systems, on the other – climate change. Further work with the database assumes, on the basis of available quantitative data and methods of mathematical statistics, the identification of elements of the ecological, socio-economic systems of the KhMAO and YaNAO vulnerable to global climate change, as well as forecasting the dynamics of their (systems) transformation.

Intelligent soft package for modeling the planning process of multi‑assortment industrial production

The article discusses issues related to the development of a flexible intelligent software package for solving the problem of optimal planning of multi-assortment production. These industries are characterized by a large range of products, many types and configurations of equipment, with an increase in the dimension of the problem, the number of options for production schedules grows exponentially, therefore, it is extremely important to develop a specialized complex for effective optimal planning and scheduling, insisting on the characteristics of various multi-assortment industries. The purpose of this work is to increase the productivity of multi-assortment enterprises and reduce the time of production of products by developing methods and algorithms for optimizing scheduling in the form of a problem-oriented software package. The article presents a mathematical formulation of the optimization problem and a set of mathematical models and algorithms for the formation of objective functions for optimal scheduling of reconfigurable productions. Conducting this study is based on the use of methods of scheduling theory, optimization and evolutionary calculations, tools for object-oriented development of complex software systems and databases. The proposed software package has various intelligent user interfaces, supplemented by databases of products, equipment and technological regulations, a library of objective functions and mathematical optimization methods, an expert system tuning module, as well as an interactive system for visualizing the resulting production plans in the form of a Gantt chart and decision tree of the optimization problem. Testing of the software package was carried out on the data of polymer and metallurgical enterprises in Russia and Germany and confirmed the effectiveness of solving planning problems. Implementation of the proposed software package makes it possible to ensure efficient loading of enterprise equipment, reduce production costs and simplify the process of making managerial decisions in the course of production planning.