The development of an automatic system for installing a magneto-optical magnetic field sensor in a neutral position has been carried out. Installation in the neutral position is carried out by an automatic piezoelectric drive control system based on a microcontroller. A mathematical model is constructed and numerical simulation of the automatic control system is performed. The results and parameters of the simulation are presented. The developed system provides a significant increase in the autonomy of the sensor, which makes it possible to eliminate or significantly reduce the cost of regulating the sensor.

Keywords: magnetic field sensor, magneto-optical sensor, automatic control system, piezo motor

The article describes an approach to the operation of a data transmission network protection system against computer attacks based on a hybrid neural network. It is proposed to use a hybrid neural network as a machine learning method. To calculate the output value of neural network signals, the activation function is used. The neural network model consists of recurrent cells - LSTM and GRU. Experiments have demonstrated that the proposed network protection system for detecting computer attacks based on an assessment of the self-similarity of the system functioning parameters using fractal indicators and predicting the impact of cyber attacks by applying the proposed structure of the LSTM neural network has a sufficiently high efficiency in detecting both known and unknown spacecraft. The probability of detecting known spacecraft is 0.96, and the zero-day attack is 0.8.

Keywords: data transmission network, computer attack, neural network, protection system, network traffic, auto-encoder, accuracy, completeness, detection, classifier, self-similarity, recurrent cells with long short-term memory

One of the main conditions for ensuring information security is to prevent the spread of false and intentionally distorted information. Filtering the content of Internet information resources can serve as a solution to this problem. Recently, an approach using methods and mathematical models of artificial intelligence has been increasingly considered for the analysis and classification of disseminated data. The use of neural networks allows you to automate the process of processing a large array of information and connect a person only at the decision-making stage. The paper focuses on the learning process of a neural network. Various learning algorithms are considered: stochastic gradient descent, Adagrad, RMSProp, Adam, Adama and Nadam. The results of the implementation of text subject recognition using a recurrent neural network of the LSTM model are presented. The results of computational experiments are presented, an analysis is carried out and conclusions are drawn.

Keywords: information security, text analysis, artificial intelligence method, artificial neural network, recurrent LSTM network