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Proposals to improve the information capabilities of coastal-based radar stations for surveillance of surface and air objects
(Національний технічний університет "Харківський політехнічний інститут", 2024) Kuznietsov, Oleksandr; Kolomiitsev, Oleksii; Nos, Ivan; Biesova, Oksana; Krykhovetskyi, Heorhii
Sea-based radar stations (RS) are widely used for solving the tasks of radar surveillance of surface objects (SO) and air objects (AO). The subject of the article is the mechanisms of radio wave propagation in the boundary layer of the atmosphere. The aim is to investigate the possibilities of improving the accuracy of measuring the range and radial velocity of SO and AO observed beyond the line-of-sight of coastal-based RS. Objective: to analyse the spatial and temporal parameters and properties of waveguide layers above the water surface. Methods used: maximum likelihood and frequency. The following results were obtained. The results of experimental studies of seasonal and daily changes in the parameters of the lower troposphere layer in the Black Sea coastal zone and the parameters of tropospheric radio waveguides are presented. The procedure for calculating the energy transmission losses during radio wave propagation in the boundary layer of the atmosphere is presented, and the conditions for detecting SO and AO beyond the radar line-of-sight are determined. Recommendations for increasing the range of detection of SO and AO are given, which are associated with the possibility of predicting the existence of tropospheric radio waveguides by using data on the current conditions of radio wave propagation over the sea based on the signals of the automatic ship identification system AIS. Conclusions. Proposals have been developed to improve the accuracy of measuring the range and radial velocity of SO and AO at waveguide propagation of radio waves over the sea surface. A promising area for further research may be to identify ways to optimise the measurement of angular coordinates in modern RS during waveguide propagation of radio waves over the sea surface.
Designing and evaluating DL-model for vulnerability detection in smart contracts
(Національний технічний університет "Харківський політехнічний інститут", 2023) Shmatko, Oleksandr; Kolomiitsev, Oleksii; Rekova, Nataliia; Kuchuk, Nina; Matvieiev, Oleksandr
Task features. Smart-contracts are programs that are stored in a distributed registry and execute code written in them in response to transactions addressed to them. Such smart- contracts are written in the Solidity programming language, which has a specific structure and syntax. The language was developed for the Ethereum platform. Having a specific structure, such languages are prone to certain vulnerabilities, the use of which can lead to large financial losses. Task statement. In this paper, a Deep Learning (DL) model is used to detect the vulnerabilities. Using the chosen approach and a properly specified input data structure, it is possible to detect complex dependencies between various program variables that contain vulnerabilities and bugs. Research results. Using well-defined experiments, this approach was investigated to better understand the model and improve its performance. The developed model classified vulnerabilities at the string level, using the Solidity corpus of smart-contracts as input data. The application of the DL model allows vulnerabilities of varying complexity to be identified in smart-contracts. Conclusions. Thus, the pipeline developed by us can capture more internal code information than other models. Information from software tokens, although semantically incapable of capturing vulnerabilities, increases the accuracy of models. The interpretability of the model has been added through the use of the attention mechanism. Operator accounting has shown significant performance improvements.

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