articles
| This article shows how the modified U-Net, a neural network, can be used to find differences in visible and radio frequency spectrum images. The neural network was modified, with its convolutional layers replaced by the convolution blocks with neuromorphic microbiological cells, which partially destroy the cellular skeletal structure and change their conductivity through controlled biocorrosion. The author developed a method of training a modified neural network based on stimulation of the bacterial layer for the corrosion of conductive components. Functional analysis demonstrated the high efficiency of neural network element configuration and showed that the elements can form interconnected active structures. The author found out that, thanks to the neural network cell’s feature, neutral units can autogenerate signals. This is how information passing through the network can be processed both in passive mode and through interaction with local electrical activity. The author also researched generated activities, which revealed the integral effect of adding signals from neuromorphic cells, resulting in a complex response that includes the spectral components of all neighbouring cells. The modified network has an advantage over similar neural network structures: training can be managed by changing the total activity of neurons, rather than by evaluating the network’s response to test data. When it comes to a trained and formed neural network in which conductive structures are configured, spontaneous activity occurs much less frequently than in the initial configuration where the cells were not subjected to biocorrosion and therefore had maximum conductivity. The experiments demonstrated that the modified U-Net can be used to find differences in visible and radio frequency spectrum images. To successfully find differences hidden by the geometric features of the terrain, the author used a comprehensive strategy for image comparison using visible and radio spectra. The practical research is novel in that it offers a newly developed modification of neuromorphic cells. They achieve high speed of task solution due to the massively parallel organisation of detecting changes in images. Continue... | |
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№ 5(119)
24 october 2025 year
Rubric: Data protection Authors: Kotenko I., Mityakov E. S., Saenko I. |
Currently, the problem of ensuring information security of critical information infrastructure is steadily increasing and acquiring strategic importance, which is caused by the explosive growth of complex targeted attacks on infrastructure facilities. The solution to this problem requires the development of new approaches for assessing information security threats that combine the relevance of data provided by threat intelligence technology with a deep understanding of the specifics of the protected systems. An analysis of the state of the problem shows that existing approaches for assessing information security threats to critical information infrastructure facilities have such shortcomings as a gap between threat intelligence data and the context of a specific system, subjectivity of qualitative assessments, and the complexity of ranking threats given many conflicting criteria. To overcome these shortcomings, the article proposes a method for multi-criteria assessment of information security threats to critical information infrastructure facilities that integrates threat intelligence and digital twin technologies, where the digital twin technology is designed to provide the necessary understanding of object specifics. A system of indicators has been developed, structured according to five projections of threat assessment: severity of consequences, intruder capabilities, vulnerability of the facility, complexity of the attack, and effectiveness of protection. A conceptual model of an information security threat assessment system based on the technologies of digital twins and threat intelligence has been developed. A multi-criteria threat assessment methodology is presented, in which the integral threat index and Pareto-optimal threat ranks are calculated based on a set of criteria. Experimental testing on synthetic data confirmed the consistency of the results of these calculations. Practical application of the proposed method allows for threat analysis both as a whole and within individual projections of the indicator system. Continue... |
| The research is devoted to solving the problem of developing a method for protecting cloud platforms of critical information infrastructure based on cyber immunity. The analysis of existing approaches to protecting various digital systems has been conducted. It has been established that current approaches do not fully account for the specific characteristics of critical information infrastructure cloud platforms, namely: complex multi-layered architectures; the potential presence of undetected vulnerabilities leading to previously unknown threats of violation of computational semantics; elevated requirements for resilience; and the necessity for restoration of normal operation. The research sets out the objective of developing a new method for protecting cloud platforms of critical information infrastructure based on cyber immunity. A hypothesis has been formulated, stating that ensuring the required level of resilience for cloud platforms under cyberattacks is possible by adjusting the countermeasure parameters within a range of necessary and sufficient values, defined with consideration of the aforementioned requirements. The idea has been substantiated and the method for protecting cloud platforms of critical information infrastructure with cyber immunity has been developed. The method ensures the resilience of cloud platforms under computer attacks by varying the cyber immunity coverage ratio, taking into account the probability of achieving operational goals and the full execution time of program cycles. The scientific novelty of the proposed method lies in the fact that a modified bisection method has been applied to find the required value of the cyber immunity coverage coefficient. Furthermore, a criterion for verifying the existence of a necessary and sufficient value of this coefficient has been substantiated and implemented for the first time. Theoretical and experimental studies of the developed method have been conducted, confirming the proposed hypothesis. Continue... | |
| Unmanned aerial vehicles are currently widely used in maritime search and rescue missions to survey waters and locate victims. The success of the missions largely depends on the effectiveness of the search strategy. The article proposes a deterministic method for delimiting search areas and planning flight trajectories for a group of unmanned aerial vehicles during maritime search operations. An unmanned aerial vehicle detects a target if it is within the coverage area of the remote sensing equipment installed on the vehicle. The coverage area is specified as a circle of a certain radius. The problem of planning trajectories for complete coverage of the search area is solved. A trajectory of minimum length is considered effective. Two search strategies are considered: without taking into account and taking into account restrictions on energy resources. The problem of complete coverage of the search area divided into sectors is solved. Each sector is assigned to one device, which searches for a target in its sector according to a given algorithm. A geometric model of the search trajectory is presented. Four algorithms implementing the two specified search strategies are presented: 1) an algorithm for deploying a group of devices from the central point of the search area without taking into account energy resource constraints; 2) an algorithm for deploying a group of devices from the approach point to the search area without taking into account energy resource constraints; 3) an algorithm for deploying a group of devices from the central point of the search area taking into account energy resource constraints; 4) an algorithm for deploying a group of devices from the approach point to the search area taking into account energy resource constraints. The algorithms are tested. Continue... | |
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№ 6(120)
30 december 2025 year
Rubric: Software engineering Authors: Minin V., Filimonova E., Kakatunova T., Kirillova E. |
Anomaly detection is a pressing research problem in many subject areas, the solution of which enables timely management decision-making. This study proposes a method for identifying anomalies in economic indicators characterizing the internal and external environment of a manufacturing organization. This method can be applied in the algorithmic support of business decision support systems. The method is based on the use of an artificial neural network with an autoencoder architecture trained to replicate input data at the output. After training the autoencoder on normal data, the error in reconstructing the input at the output will be small. However, when fed anomalous data, the error will increase, which can serve as an anomaly indicator. The proposed method uses a convolutional autoencoder, so the input data is first converted into images (signatures), for which an original method for their formation is proposed. The method involves representing the historical behavior of each economic indicator as a heat matrix. Each heat matrix forms one channel, and their combination forms a signature, which is then fed to the autoencoder input for further analysis. The autoencoder utilizes depthwise separable convolutions, allowing for autonomous tuning of convolutional filters for individual signature channels. The novelty of the research results lies in the developed method for detecting anomalies in economic indicator arrays, which enables localization of collective and individual anomalies (outliers), as well as in the developed software used to test the method. Computational experiments demonstrated that the method achieves anomaly detection accuracy comparable to some modern models. Continue... |
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№ 6(120)
30 december 2025 year
Rubric: Algorithmic efficiency Authors: Golikov R., Korostelkina I., Lyapina I., Meksheneva Z. |
In the field of digital signal processing, restoring their shape at a high level of noise component is one of the main problems. Its relevance is due to the widespread use of digital technologies and it becomes particularly acute in those areas where interference inevitably affects the registration quality, recognition, and signals interpretation. A common type of naturally occurring interference is thermal noise, which is directly related to the measuring operation and recording equipment. It is impossible to completely eliminate this noise kind, but modern digital processing methods are capable of significantly reducing its negative impact. Currently, researchers’ attention is increasingly focused on developing heuristic algorithms that represent alternative ways of suppressing the noisy component while preserving the useful signal’s form. These algorithms are characterized by their ability to find approximate solutions where traditional analytical and technical methods lose their effectiveness. They are aimed at adapting to the stochastic nature of thermal noise and offer a reasonable compromise between labor intensity and the useful signal reproduction accuracy. This article continues previous published research into the heuristic algorithms development for recovering the shape of heavily distorted discrete signals. The goal is to propose an alternative approach to solving this problem based on the sequential application idea of numerical integration and differentiation operations combined with integral curve approximation procedure. As a result, the noise component influence is eliminated, and the restored signal retains information components of the useful signal. The proposed algorithm efficiency was determined using a test signal superimposed with artificial noise simulated via computer simulation of a pseudo-random number generator. The results were compared with two previously developed heuristic algorithms: one based on piecewise linear approximation by least squares method and another based on averaging instantaneous values of the signal over partition intervals. Analysis demonstrated that the developed algorithm compares favorably in terms of accuracy with these algorithms, but differs in greater efficiency when processing discrete nonperiodic signals with natural noise contamination. Continue... |
