Statement and Solution of Multicriteria Tasks of Database Modular Block-Schemes Development

The paper considers developed and offered an effective algorithm for solving the block-symmetrical tasks of polynomial computational complexity of data processing modular block-schemes designing.Currently, there are a large number of technologies and tools that allow you to create information systems of any class and purpose. To solve the problems of designing effective information systems, various models and methods are used, in particular, mathematical discrete programming methods. At the same time, it is known that such tasks have exponential computational complexity and can not always be used to solve practical problems. In this regard, there is a need to develop models and methods of the new class, which provide the solution of applied problems of discrete programming, aimed at solving problems of large dimensions. The work has developed and proposed block-symmetric models and methods as a new class of discrete programming problems that allow us to set and solve applied problems from various spheres of human activity.The issues of using the developed models are considered. and methods for computer-aided design of information systems (IS)

Optimization Model of Adaptive Decision Taking Support System for Distributed Systems Cyber Security Facilities Placement

Abstract— An article herein presents an optimization model, designated for computational core of decision-taking support system (DTSS). DTSS is necessary for system analysis and search of optimal versions for cybersecurity facilities placement and information protection of an enterprise or organization distributed computational network (DCN). DTSS and a model allow automize the analysis of information protection and cybersecurity systems in different versions. It is possible to consider, how separate elements, influence at DCN protection factors and their combinations. Offered model, in distinction from existing, has allowed implementing both the principles of information protection equivalency to a concrete threat and a system complex approach to forming a highly effective protection system for DCN. Hereby we have presented the outcomes of computational experiments on selecting the rational program algorithm of implementing the developed optimization model. It has been offered to use genetic algorithm modification (GAM). Based on the offered model, there has been implemented the module for adaptive DTSS. DTSS module might be applied upon designing protected DCN, based on preset architecture and available sets of information protection and cybersecurity systems in the network

DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites