Problems of Cybercrime in the Era of Hypervolatility: Legal Aspect

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Abstract

At the current stage of socio-economic development, the problem of cybercrime has become one of the most acute and relevant issues. While recognizing its indisputable importance in application-oriented terms, it is impossible to ignore its fundamental scientific and theoretical complexity. To a large extent, this is due to the vagueness and uncertainty of the fundamental terminological foundations of the concept. Additional sophistication and complexity of this issue is due to the unique peculiarities of the legal regulation mechanisms in the field of innovation and digital information technologies. The cyber economy and data economy are characterized by increased volatility (hypervolatility) and an extremely short retrospective. Therefore, the legal norms characterized by a precedent nature are either ineffective or simply inapplicable. This article formulates and substantiates the argument of the need for a proactive approach focused not on the retrospective that concerns past violations, but taking into account the objective trends in the development of the cyber sphere (new information technologies).

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About the authors

Anna V. Yakovleva

The Herzen State Pedagogical University of Russia

Author for correspondence.
Email: yeif@mail.ru
ORCID iD: 0000-0001-9219-9405
SPIN-code: 4603-0174

Dr. Sci. (Econ.), Professor, Associate Professor

Russian Federation, Saint Petersburg

Pavel V. Konyukhovskiy

The Herzen State Pedagogical University of Russia

Email: kon_pv@mail.ru
ORCID iD: 0000-0002-2940-1049
SPIN-code: 9637-6933

Dr. Sci. (Econ.), Professor

Russian Federation, Saint Petersburg

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Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. Largest fines collected for breach of privacy in the world as of April 2024, USD million.

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3. Fig. 2. Structure and main concepts of the “Model Law on Combating Cybercrime”, 2023.

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4. Fig. 3. Key functions of the FBI Internet Crime Complaint Center (IC3).

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5. Fig. 4. Grouping of types of Internet crimes.

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6. Fig. 5. Average values ​​of financial losses by cybercrime groups.

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7. Fig. 6. Average values ​​of financial losses for individual types of cybercrimes.

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8. Fig. 7. Dynamics of relative changes in financial losses (by major groups of cybercrimes).

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9. Fig. 8. Dynamics of places in the ranking by types of cybercrimes.

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10. Fig. 9. Standard deviations by cybercrime rankings.

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11. Fig. 10. Standard deviations in the rating (by major groups of cybercrimes).

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12. Fig. 11. Correlogram for the US information base (see Table 4)

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13. Fig. 12. Regression model for the number of complaints about violations in the cyber sphere (US statistics).

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14. Fig. 13. Correlogram for the information base for Russia (see Table 5).

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15. Fig. 14. The share of cybercrimes in the total number of crimes (data for Russia).

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16. Fig. 15. Regression model for the number of crimes in the cyber sphere (statistics for Russia).

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