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

The article discusses the opportunities that arise when using data mining methods in construction. A numerical experiment is carried out to create an algorithm for making design decisions in the production of zero-cycle works, namely, when installing the enclosing structures of the pit edge. Comparison of the data obtained in the Deductor Studio software product with those actually used in the construction of a residential building is performed.

Keywords: construction, earthworks, information technology, decision tree, design decision making, data analysis, machine learning, Data Mining, Deductor.