Cybercrime as a Threat to the National Security of the Baltic States and Ukraine: The Comparative Analysis
Bibliographic record
Abstract
The article is devoted to comparative legal research of cybercrime as a threat to the national security of the Baltic States and Ukraine. The purpose of the scientific article is to study the systems of criminal legislation in the part of cybercrime in the Baltic States and Ukraine, to determine the public danger of cybercrime, as well as to study ways of countering this criminal category. The object of research is cybercrime as a criminal and legal category. The subject of the study is the peculiarities of combating cybercrimes in the Baltic States and Ukraine, and carrying out a comparative analysis of their criminal legislation. The authors analyze the essence of the concept of cybercrime and identify the main types of cybercrime that are committed most often. Thus, in this article, "cybercrime" means any crime committed with the help of information technologies or in the information space. Special attention is focused on cybercrime in the Baltic States, in particular Latvia, Lithuania, and Estonia. The main types of cybercrimes, the responsibility for the commission of which is provided for by Chapter XVII of the Criminal Code of Ukraine, are described. The Convention on Cybercrime was considered, and it was determined which norms should be implemented in the legislation of the Baltic countries and Ukraine. It was determined that the issue of countering cybercrime in the Baltic States. The main threats, prevention, and countermeasures against cybercrimes in the Baltic States and Ukraine and the main features of international and legal cooperation with cybercrimes within the framework of the European Union are outlined. It was concluded that in order to combat crimes committed with the use of modern information technologies in Ukraine, it is necessary to constantly increase the security of information systems, develop modern information technologies, improve legislation in the field of information crimes, develop competitive means of informatization, expand international cooperation in the field of safe use of information resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".