• L. O. Vlasenko State University of Trade and Economics, Kyiv, Ukraine, Ukraine
  • N. M Lutska National University of Food Technologies, Kyiv, Ukraine, Ukraine
  • N. A. Zaiets National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine, Ukraine
  • T. V. Savchenko State University of Trade and Economics, Kyiv, Ukraine, Ukraine
  • A. A. Rudenskiy National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine, Ukraine



ontology, digital forensic, digital crime, digital evidence


Context. A feature of the modern digital world is that crime is often committed thanks to the latest computer technologies, and the work of law enforcement agencies faces a number of complex challenges in the digital environment. The development of information technology and Internet communications creates new opportunities for criminals who use digital traces and evidence to commit crimes, which complicates the process of identifying and tracking them.

Objective. Development of an applied ontology for a system for analyzing a digital criminal offense, which will effectively analyze, process and interpret a large amount of digital data. It will help to cope with the complex task of processing digital data, and will also help automate the process of discovering new knowledge.

Methods. To build an ontological model as a means of reflecting knowledge about digital crime, information was collected on existing international and domestic classifications. The needs and requirements that must be satisfied by the developed ontology were also analyzed. The creation of an ontological model that reflects the basic concepts, relationships in the field of digital criminal offense was carried out in accordance with a recognized ontological analysis of a specialized subject area.

Results. An applied ontology contains the definition of entities, properties, classes, subclasses, etc., as well as the creation of semantic relationships between them. At the center of the semantics is the Digital Crime class, the problem area of which is complemented by the interrelated classes Intruder, Digital evidence, Types of Crime, and Criminal liability. Such characteristics as motive, type of crime, method of commission, classification signs of digital traces and types of crime, as well as other individual information were assigned to the attributes of the corresponding classes. An ontological model was implemented in OWL using the Protégé software tool. A feature of the implementation of the applied ontology was the creation of SWRL rules for automatically filling in additional links between a class instance. Manual and automatic verification of the ontology showed the integrity, consistency, a high degree of correctness and adequacy of the model. The bugs found were usually related to technical aspects and semantic inconsistencies, which were corrected during further development iterations.

Conclusions. The research confirmed the effectiveness of the developed applied ontology for the analysis of digital criminality, providing more accurate and faster results compared to traditional approaches.

Author Biographies

L. O. Vlasenko, State University of Trade and Economics, Kyiv, Ukraine

PhD, Associate Professor of the Department of Software Engineering and Cyber Security

N. M Lutska, National University of Food Technologies, Kyiv, Ukraine

Dr. Sc., Professor of the Department of Automation and Computer Technologies of Control Systems

N. A. Zaiets, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

Dr. Sc., Professor of the Department of Department of Automation and Robotic Systems

T. V. Savchenko, State University of Trade and Economics, Kyiv, Ukraine

PhD, Associate Professor of the Department of Software Engineering and Cyber Security

A. A. Rudenskiy, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

Senior lecturer of the Department of Automation and Robotic Systems


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How to Cite

Vlasenko, L. O., Lutska, N. M., Zaiets, N. A., Savchenko, T. V., & Rudenskiy, A. A. (2024). DEVELOPMENT OF APPLIED ONTOLOGY FOR THE ANALYSIS OF DIGITAL CRIMINAL CRIME . Radio Electronics, Computer Science, Control, (4), 184.



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