name
Chastikova Vera Arkadyevna
Scholastic degree
•
Academic rank
associated professor
Honorary rank
—
Organization, job position
• Kuban State Technological University
кафедра компьютерных технологий и информационной безопасности
Research interests
системы искусственного интеллекта, генетические алгоритмы и эволюционное программирование, нейронные сети.
Web site url
—
Current rating (overall rating of articles)
0
TOP5 coauthors
Articles count: 9
Сформировать список работ, опубликованных в Научном журнале КубГАУ

HYBRID OPTIMIZING GRIFFONVULTURE ALGORITHM BASED ON SWARM INTELLIGENCE MECHANISMS
DescriptionGriffonvultures with input parameters minimal value for compound functions optimization that change during the time searching hybrid algorithm offered in this article. Researches of its efficiency and comparing analysis with some other systems have been performed

Description
In this article the identification and the research of genetic algorithm key parameters of genetic schemes method and their influence on efficiency of optimum decisions search in expert systems of production type is conducted. The following parameters of genetic algorithm are considered: crossover operator, choice of parents pare, mutation operator, inversion operator

Description
This article is devoted to the research of influence of genetic algorithm key parameters of genetic schemes method on efficiency of optimum decisions search in expert systems. The following parameters of genetic algorithm are considered: number of population, length of binary codes, the mechanism of parental pairs selection, the choice of the reproduction scheme

COMPREHENSIVE RING SYSTEM OF PROGRAM PROTECTION AGAINST USING BY ILLEGAL USERS
DescriptionThis article shows the algorithm of the realization of multilevel system of program protection against using by illegal users. The proposed system consists of 4 protection levels

METHOD OF POLYMORPHIC VIRUSES DETECTION BASED ON ARTIFICIAL IMMUNE SYSTEM AND GENETIC ALGORITHMS
DescriptionThis article is dedicated to the study of the fundamental properties and components of the immune system such as B lymphocytes, the Tlymphocytes, immune system storage, primary and secondary immune response, immunological training detectors, which will be the basis of the obtained as a result of detection methods of polymorphic viruses using artificial immune systems. Polymorphism of computer viruses is the formation of a malicious program code directly during execution. Thus, it is impossible to create a unique signature corresponding to these polymorphic viruses. A similar classification problem is solved by the immune system of vertebrates, stared again met with the virus, it "remembers" him, and the next time provides effective secondary immune response. These properties of the immune system served as a prerequisite for the use of immune approaches and algorithms for solving the problems of detection of malicious code. The article identified and described their main features, proposed the idea of their implementation and software, system interactions in the immune system revealed such important features, the implementation of which will be effective in solving the problem of detection of malicious code and software. Also, for a more productive system of education is considered a class of genetic, evolutionary algorithms, described by their immediate implementation of sitespecific decentralized artificial immune system, built a system of interaction of genetic and immunological algorithms.

METHODIC OF KNOWLEDGE ASSESSMENT WITHIN THE FRAMEWORK OF THE AUTOMATED TESTING SYSTEM
DescriptionIn this article, we consider approaches to the transfer of knowledge to students and an objective semiautomatic assessment of knowledge. The characteristic features of the application and the possibility of using cognitive training methodologies and complex systems for testing skills and the theoretical base of trainees are analyzed. The problems of development of this direction and possible ways of their solution are described. The basic concepts are introduced and the existing methods of calculating the average score for checking the student's knowledge are considered, and a new approach to solving this problem is proposed. Based on the conducted researches it is offered to use the complex system of testing of end users, which includes testing, monitoring, collecting, analyzing and displaying the results of students/groups/ course. The main requirements for the creation of such a complex and the rules to be followed are formulated for a more objective assessment of knowledge. A model of an integrated modular system for objective semiautomatic testing of knowledge through testing is described

Description
This article is dedicated to the study of the parameters of the artificial immune system for solving the polymorphic viruses’ detection problem. The goal is to define a vector of the immune system parameters that would ensure the minimum number of errors of the first kind, the minimum number of errors of the second kind and the maximum percentage of polymorphic viruses’ detection. That is, the most accurate classification of them as a malicious code, in relation to any theoretically possible vector of parameters of the artificial immune system. A distinctive feature of the studied artificial immune system is the use of a class of genetic algorithms that provide more efficient training of detectors. The configurable parameters of the system are: the algorithm for determining the proximity of the detector and the pathogen, which can be realized by determining the Levenshtein distance or by the method of adjacent bits; as well as the method of implementing the crossingover operator, the method of implementing the mutation operator, the method of implementing the selection operator, the algorithm for determining the proximity of the detector lines. In addition, the article considers the expediency of using a distributed network of several nodes, each of which will have an immune system that will exchange data with other nodes of the network. As a result of the research, a set of optimal parameters was obtained in which the system achieves the maximum accuracy of recognition of polymorphic viruses

Description
This article is devoted to the problem of network attacks recognition, which is essential for providing network security. A research of neural network efficiency has been held. Such metaeuristic algorithms as genetic algorithm, gray wolf algorithm and firefly algorithm have been applied for the neural network learning. The algorithms’ fundamentals have been described. Multilayer perseptrone with sigmoid activation function has been selected for the task of network attack presence check. Various configurations of the neural network have been tested in order to find the optimal number of layers and neurons per layer, which ensure the least error. Learning has been performed by minimization of the average squared error between the network’s output and its target value with the help of the listed algorithms. Genetic algorithm requires accurate parameter picking in case of any network’s architecture alteration. Moreover, it is not as fast as firefly and gray wolf algorithms. Gray wolf algorithm appears to be the most effective one. However, it loses its efficiency if the number of layers is increased. Firefly algorithm proves to be the most universal one. Although it is less effective than gray wolf algorithm, it provides the most exact output even if the network’s structure is changed