A methodology has been developed for processing and analyzing satellite ozone data and constructing digital maps of the TO field in the Southern Hemisphere over the geodynamic regions of Antarctica. An analysis of the original ozone data from August to October for the period 2000–2023 was carried out, and missing TOC values were restored. Based on the results of the analysis, tables were generated containing information on TO minima with geographic coordinates, ozone mass deficit and the area of the Antarctic Ozone Hole (AHO) for specified dates. The locations of TOC minima for 1978–2023 are presented on digital maps of Antarctica. A detailed analysis of anomalous ozone variations over Antarctica in 2002, 2018 and 2019 was performed. The AHO area during this period varied from 3.64 million km2 (October 31, 2019) to 9.41 million km2 (October 15, 2019).

Access to the Darknet is carried out through special technologies that provide anonymity and confidentiality of users. In addition to access to legitimate resources, such tools can be used for illegal purposes, such as the sale of data, weapons and prohibited substances. Therefore, the analysis of the Darknet traffic, as well as the detection and classification of hidden services are important for preventing potential security threats. The most promising tool for automated monitoring of network traffic data is artificial intelligence technology. This study proposes an approach for darknet traffic analysis based on graph neural networks. This type of deep learning considers both structural and causal relationships between network events. A comparative analysis of several models of graph neural networks at the dataset of Darknet traffic is presented.

The article considers the problem of choosing the optimal combination of identification technologies for a class of cyberphysical systems controlling technological processes, the specifics of which do not allow using the same identification method at all technological stages. A formal statement of the problem is formulated. The criteria, restrictions and conditions for searching for optimal combinations are determined. The solution of the problem is considered on the example of a mobile equipment turnover control system. A simulation model is described, created to generate a full space of accept- able combinations of identification technologies, calculation of parameters, simulation of the technological process and the search for a quasi-optimal solution. The structure of the model, the main modules and functions are revealed. The space of acceptable configurations obtained as a result of modeling is analyzed. The results of the search for optimal solutions and their dependence on the parameters and constraints of the model are shown.

A hybrid algorithm for solving the problem of placing VLSI elements has been developed, which combines the procedures for the behavior of a group of bees and a swarm of chromosomes. The optimization criterion is based on estimates of the number of nets crossing the given lines of the routing area. A modified chromosome swarm paradigm is described, which, in contrast to the canonical method, provides the possibility of searching for solutions in an affine position space with integer parameter values. As the weight of the affine connection between chromosomes on the affine-relaxation model of a swarm of chromosomes, estimates of the degree of «difference» between them are used. A modified structure of the bee algorithm is proposed. For each base position, a probabilistic choice of a set of positions located in its vicinity is implemented. The time complexity of the algorithm is О(n2) – О(n3).

The article formalizes the optimization problem of synthesizing the structure of secure information exchange, analyzes the application of known approaches, defines the main stages and principles of a solution to ensure the security of a mobile device.

The development of the modern economy associated with the development of natural resources is greatly influenced by information technologies of the platform type. In this area the formation of new economic entities of business structures of the resource type is taking place – ecosystems capable of solving new problems on the principles of the digital economy. The work is devoted to the methodological and technological aspects of the development and formation of re- source-type business systems. The possibilities of platform interaction of business systems of a resource type are considered on the example of a resource region (Krasnoyarsk Territory). The key idea of the study: the basis for the development of resource-type business ecosystems in a resource-rich region is cooperation and network interactions which are effectively analyzed within the framework of the author›s technology – functional-environmental analysis.

the problem of generalized integration of three information systems’s algebraic models can be solved in different ways. In this article two different approaches to it’s solution are explained on the certain example of this type’s three models: G1/7, G2/5, G3/6. The first approach is based on two-step usage of scheme GNI, the second – on usage scheme QSI3 and usage’s subsequent series of scheme HBI.

The article comprises issues of analytical modeling of attacks in wireless sensor networks. A proposed attack model specifies possible types of unauthorized influences at the network level aimed at compromising network nodes, data transmitted over the network, and information services provided by the wireless sensor network. A developed graph-based analytical model of a wireless sensor network aims at detecting network layer attacks by processing actual events received from network nodes and deciding whether the network is running under an attack. The correctness of the models is confirmed by their testing by using an example of a scenario for detecting a wormhole attack aimed at disrupting routing processes in a wireless sensor network.

The article gives a general idea of intrusion detection systems (IDS) and machine learning, presents a classification of machine learning methods. The most commonly used data sets for training IDS are considered. The main performance indicators used to assess the IDS are described. A review of modern research on the development of intrusion detection methods using machine learning is made, the strengths and weaknesses of each solution are described. The data sets used in them, the obtained estimates of the effectiveness of the proposed intrusion detection methods, are analyzed. Based on the results of the analysis, development trends, unresolved problems and promising areas of research were identified.

Quantum neural network (QNN) is one of the promising areas in which noisy medium-scale quantum devices (MCD) can find advantageous applications in the short term compared to classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but there is still no canonical model of a quantum recurrent neural network (QRNN), which certainly limits research in the field of quantum deep learning. In the present paper, a new type of QRNS is proposed that would be a good candidate as a canonical QRNN model, where quantum recurrent blocks (QRB) are built in a hardware-efficient way, and QRNN are constructed by stacking QRB in a checkerboard pattern, which can significantly reduce the algorithm’s requirements with respect to coherent time of quantum devices. That is, QRNN is much more accessible on MCD devices. Numerical experiments show that our QRNN provides much better performance in prediction (classification) accuracy compared to classical RNN and state-of-the-art QNN models for sequential training and can predict changing details of time series data.