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• CLASSIFICATION OF PROCESSING NODES IN BIG DATA SYSTEMS ACCORDING TO THE ZERO TRUST APPROACH

  М.А. Poltavtseva , D. V. Ivanov

  55-62

  2025-07-24

  Abstract ▼

  Data cybersecurity is one of the most important factors for the successful implementation of the national project ‘Data Economy and Digital Transformation of the State’. The challenges of building secure big data systems lie in their heterogeneous nature, large number of heterogeneous tools, high connectivity and high trust between distributed components. Reducing the internal trust and reducing the attack surface according to the zero-trust approach is necessary to increase the security of such systems with the least impact on their performance. The aim of the paper is to create a method for dynamic classification of nodes and data processing components in heterogeneous big data systems based on the application of different approaches to trust reduction with respect to the objects realising the information processing process. The paper considers the zero trust approach as applied to the class of systems under study, as well as the task of extended implementation of the principle of minimum privilege to reduce the attack surface. The authors present a classification of nodes - handlers based on their operations with data, unified according to the previously developed conceptual data model. A comparison of nodes and security methods applied to them based on the need for access to semantics and data components to perform operations is proposed. Based on this classification, a method of dynamic node type determination during system operation is developed for situations of changing component composition of a big data processing system, typical for multi-component distributed highly loaded systems. The results of the work are a part of the complex consistency approach to the construction of secure big data processing systems.

• IMPROVING THE QUALITY OF MULTI-MODAL DATA BASED ON THE HYBRIDIZATION OF PREPROCESSING AND MULTISENSORY FUSION METHODS

  А. А. Aleksandrov , М.А. Butakova

  21-38

  2026-09-10

  Abstract ▼

  The article explores the methods of data preprocessing and merging of multi-modal data. The data collected from multi-sensor devices contains noise, anomalies, and includes sensor failures. The use of raw data inevitably leads to false patterns when using machine learning models in which this data is used. To improve the quality of data, it is necessary to use special methods for preliminary processing and merging. The aim of the work is to create a hybrid preprocessing and multisensory data fusion controller operating at the peripheral computing level. To achieve this, the tasks of removing the consequences of technical failures and noise from the data, time synchronization, scaling of features and combining duplicate sources, considering their dynamic weight, have been solved. The following methods were applied: inter-quartile scale to detect outliers, linear interpolation to replace them, high-pass filter to suppress noise, and weighed multisensory fusion with dynamic weight calculation. In addition, methods for processing visual, sensory, and acoustic data within a hybrid pipeline are presented that can be adapted for implementation at the peripheral computing level. The experimental verification of the developed pipeline was carried out on a simulated set of climatic data with artificially introduced noises and anomalies.
  The proposed approach has demonstrated high efficiency of data recovery. For air temperature, the coefficient of determination (R2) increased from 0.9807 to 0.9964 with a slight change in the mean absolute percentage error (MAPE) from 10.11% to 10.48%. For relative humidity, the R2 metric increased from 0.9248 to 0.9675, and the MAPE error decreased from 2.87% to 2.57%. The most significant improvement has been achieved for atmospheric pressure. The R2 metric increased from a negative value of -0.124 to 0.974, and the MAPE error decreased from 0.12% to 0.06%. The results confirm that the proposed pipeline reduces the error and improves the quality of multisensory data, minimizes the amount of data being transferred. The practical value lies in creating a continuous cycle of local filtering and merging of data without accumulation of errors.

• MODERN APPROACHES TO NATURAL FIRE MONITORING AND FORECASTING: REVIEW AND CONCEPT OF AUTONOMOUS UAV-BASED SYSTEM

  N.D. Boldyrev , V. V. Gilka , А.S. Kuznetsova , D.А. Morozov

  58-80

  2025-12-30

  Abstract ▼

  Natural fires cause serious damage to ecosystems, the economy, and public safety every year, and timely detection of fires and prediction of their development increases the speed of response to threats and allows for optimal allocation of resources during emergency response. Existing monitoring methods are limited by the speed of detecting fire outbreaks and the speed of their further spread, which reduces the effectiveness of rescue services. To solve this problem, heterogeneous data sources can be used, including unmanned aerial vehicles (UAVs), distributed sensor networks, mobile field observation systems, ground-based thermal imaging stations, etc., which can contribute to a more accurate analysis of the current situation and improve the reliability of predictive models of fire spread. The aim of the study was to develop a concept for an automated approach to monitoring and predicting wildfires based on unmanned aerial vehicles. We believe that this approach will improve the speed of detecting fire outbreaks and the accuracy of predicting their spread. The tasks include analyzing existing monitoring methods, developing a concept for a system that integrates multispectral imaging, optimized data transmission, automatic segmentation, and forecasting based on machine learning, as well as ensuring interaction between the operator and alert specialists. The work used methods of collecting, analyzing, and transmitting data from UAVs, processing multispectral images, machine learning and neural networks for fire detection, image segmentation algorithms and simulation modeling for fire spread prediction, data visualization to support decision-making by operators and administrators, logging and analysis of results for model training, software engineering, and human-computer interaction technologies. The system will reduce the time required to detect and predict fires, enable operators to launch multiple drones simultaneously, and automate the processing of data received from them. Process automation will reduce emergency response times and staffing levels, improve resource allocation, increase forecast accuracy, and improve the timeliness of emergency service notifications. This will help reduce damage from wildfires and improve the safety of people and ecosystems. Despite the progress made in addressing this challenge, the comprehensive system described in this article does not yet exist in its entirety in Russia, the CIS countries, or in Western and Asian countries. Although individual components, such as UAVs for monitoring and artificial intelligence (AI) for data analysis, are already in active use, there is currently no integrated solution that combines all elements (drone control, near real-time fire spread prediction, data transmission, and interaction with emergency services). does not currently exist. This concept represents a new approach that could become a breakthrough technology for combating natural disasters.