The article is devoted to finding the answer to two research questions. What illegal practices are most significant for clusters of developed countries formed by similarities in trends in corruption, shadow economy, money laundering, and crime rates? What social, economic, regulatory, and digital factors most influence them in each group? The pair correlation coefficients for illicit practices indicators confirm the presence of tight and statistically significant relationships in their trends for 36 developed countries. The agglomerative clustering and canonical analysis results identified that tackling the shadow economy is crucial for Estonia, Slovenia, and Lithuania; corruption for Portugal, Hungary, Cyprus, etc.; the shadow sector and crime levels for Denmark, Norway, Finland, Sweden, and New Zealand; corruption, money laundering, and crime for Canada, Germany, the USA, etc.; four illegal practices for Italy, Greece, Turkey, Croatia, Bulgaria, and Romania. The canonical analysis revealed that social and regulatory factors influence the trends of illicit practices in developed countries more than economic and digital ones. Network analysis showed their single moderate influence in most cases. Edge evidence probability analysis confirmed a high probability of a relationship between some pairs of social, economic, regulatory, digital and illegal indicators. However, Bayesian network analysis showed a low likelihood of mutual influence of single factors, confirming the importance of the group influence.

The article analyses socio-economic profiles of countries that are victims of cybercrimes due to attacks by malicious programs and viruses spread through email applications, vulnerabilities of information systems and computer networks. The study is based on two hypotheses. The first is that powerful countries with significant global influence are both the cybercrime initiators and cybercrime victims to a greater extent than those with weak leverage. The second hypothesis is based on the fact that the level of socio-economic development of countries can be an indirect motivation for cyber criminals to commit mass cyberattacks. The proposed hypotheses were proved using cluster analysis based on the k-means and silhouette methods for the data from 93 countries. It formed 12 groups of countries based on the cyberattack volume on email applications and networks. Using the Farrar-Glauber test, the research revealed that identified vulnerabilities in information systems highly correlated with other factors. Thus, this factor was eliminated from the data set. An associative analysis was used to form a profile of the victim countries. It identified common socio-economic characteristics for each group and developed the rules of cause-and-effect relationships for them. The cluster analysis results confirm the first hypothesis that the most powerful countries, such as the USA, China, Germany, France, and others, are both victims of cyberattacks and their initiators. The analysis of profiles of countries’ clusters based on the associative rules fully confirmed the second hypothesis.