Design of an intelligent module for detecting signs of information security threats and the emergence of unreliable data

At the stage of production preparation, there is an urgent need for an automated system that would timely detect signs of threats to information security and the emergence of unreliable data. To solve this problem, an intelligent module capable of detecting such threats and unreliable and/or anomalous data has been designed. The proposed intelligent module is the state-of-art, original, and effective toolkit. It can be recommended for practical use as part of the well-known information and computer system for automated modeling of the system of automatic orientation of production objects at the stage of technological preparation of machine and instrument-building production. Its application makes it possible to increase information security and reliability of important production data at the stage of technological preparation of production, in particular, when modeling systems for automatic orientation of production objects. In addition, the use of the proposed intelligent module makes it possible to obtain a number of important social and economic effects. Some of these effects are manifested in the prevention or reduction of material, intellectual and time costs for saving and restoring information, etc. Automated analysis of important production data regarding their reliability and abnormality is carried out by machine learning methods using a specially designed advanced variational autoencoder based on classification algorithms and using wavelet transformation. The designed intelligent module for detecting signs of a threat to information security and the emergence of unreliable and/or anomalous data works in real time with a high accuracy of 97.53 %. It meets the requirements of modern production

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Publication Details

Journal
Eastern-European Journal of Enterprise Technologies
Published
2024-12-25
DOI
https://doi.org/10.15587/1729-4061.2024.317000
Primary Topic
Advanced Data Processing Techniques
Type
article
Field-Weighted Citation Impact
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article

Design of an intelligent module for detecting signs of information security threats and the emergence of unreliable data

Yurii Kyrychuk, Artem Sazonov, Irina Cherepanska, Petro Melnychuk et al.
Eastern-European Journal of Enterprise Technologies
Advanced Data Processing Techniques
article

Design of an intelligent module for detecting signs of information security threats and the emergence of unreliable data

Yurii Kyrychuk, Artem Sazonov, Irina Cherepanska, Petro Melnychuk, Dmytro Melnychuk, Nataliia Nazarenko, V. M. Pryadko, Davyd Khraban, Serhii Bakhman
article en

Abstract

At the stage of production preparation, there is an urgent need for an automated system that would timely detect signs of threats to information security and the emergence of unreliable data. To solve this problem, an intelligent module capable of detecting such threats and unreliable and/or anomalous data has been designed. The proposed intelligent module is the state-of-art, original, and effective toolkit. It can be recommended for practical use as part of the well-known information and computer system for automated modeling of the system of automatic orientation of production objects at the stage of technological preparation of machine and instrument-building production. Its application makes it possible to increase information security and reliability of important production data at the stage of technological preparation of production, in particular, when modeling systems for automatic orientation of production objects. In addition, the use of the proposed intelligent module makes it possible to obtain a number of important social and economic effects. Some of these effects are manifested in the prevention or reduction of material, intellectual and time costs for saving and restoring information, etc. Automated analysis of important production data regarding their reliability and abnormality is carried out by machine learning methods using a specially designed advanced variational autoencoder based on classification algorithms and using wavelet transformation. The designed intelligent module for detecting signs of a threat to information security and the emergence of unreliable and/or anomalous data works in real time with a high accuracy of 97.53 %. It meets the requirements of modern production

Eastern-European Journal of Enterprise TechnologiesVol. 6(2 (132))
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UA), Zhytomyr Polytechnic State University (UA)
Openalex Percentile: Top 70%
Advanced Data Processing Techniques
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