Authors
Komarcova L. G.
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Degree
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Dr of Technique, Kaluga Branch of the Bauman Moscow State Technical University |
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E-mail
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lkomartsova@yandex.ru |
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Location
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Kaluga |
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Articles
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Using neural associative machines to detect intrusion into local networksA MLP based algorithm for combining expert opinions using algorithmic composition with a dynamic mechanism to assess the coefficient of confidence in each expert is suggested. In order to increase the effectiveness of the experts the possibility of modifying the standard gradient algorithms learning methods based on the use of combined heuristics as well using experts in the Intrusion Detection System in the network is discussed. Read more...Adaptive digital data flows router based on fuzzy neural network approachWe consider the combined algorithm for radial basis neural network parameters selection based on fuzzy immune optimization algorithm. Evolutional construction of antibodies and the use of fuzzy adaptive resonance neural network are used to adapt the neural controller learning algorithm for solving the optimization problem. The possibility of using this kind of neural networks to construct an adaptive network information router and ensuring the integrity of its structure from the targeted destruction is considered.
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Analysis of the data transmission channel characteristics using neural networkThe article describes a hybrid algorithm of neural network settings using sigma-pi neurons concept.
Core of the learning algorithm is a combination of random search algorithms and heuristic algorithms.
The process of heuristic algorithm control based on an oscillating neural network is considered. An
integrated approach to neural networks training based on sigma-pi neurons allows them to perform
training for the time required to adjust the neural network to problems solving. The possibility of using
sigma-pi network to estimate the parameters of data transmission channel security based on the
analysis of the reflected signal-probing spectrum is discussed. To form the training and test sample
network a wireline analysing device was developed.
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Security of information transmission over wireless communication channels based on neural network modulesThe article refers to the possibility of using neural network security methods of digital transmission
in wireless networks. Analyzed the organization of information transfer and synchronization signals
by two groups of transmitters operating in different frequency ranges. Organization of the system
involves the transfer of synchronization signals by a group of transmitters with the same characteristics,
but located at certain points in space. As a result, the correct information sequences can be
accepted only if you know the area of the intersection of coverage zones of all clock transmitters.
For protection of the transmitted information, it is proposed to use two neural network structures. The
first neural network is constructed of two modified dynamic neural modules and a neuron with excitatory-
inhibition dynamics. Its main purpose is to produce a background of the information signal,
which is applied to conceal the information transmission, and also does not allow to determine activity
times of transmitting stations. A modified Hungarian algorithm was used to configure the neural
network and identify entry points of feedback signals was used. The second neural network structure
consist of a dynamic digital neuron. One of the structures of a neuron is a block of memory, the
content of its cells completely determines the dynamics of the development of states of the neuron.
Its primary purpose is to generate a unique signal to synchronize the whole wireless network. Configuring
of the neuron was carried out using a random search algorithm with self-learning. The main
purpose of the algorithm was to initialize the memory block of a neural element. The joint functioning
of the two developed neural network structures can protect the transmitted information without
using of scrambling methods.
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