Name
Chastikova Vera Arkadyevna
Scholastic degree
•
Academic rank
associated professor
Honorary rank
—
Organization, job position
Kuban State Technological University
Web site url
—
Articles count: 9
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
This 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
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
crossing-over 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
Griffon-vultures 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
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
This 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 site-specific
decentralized artificial immune system, built a
system of interaction of genetic and immunological
algorithms.
In 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 semi-automatic testing
of knowledge through testing is described
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