Criminophysics an application to the study of Darknet Operation
Article Sidebar
Main Article Content
Abstract
In this paper, we re-studied, with a language aimed at the Police Science community, the network of users of a child pornography forum on the Tor browser investigated during the Operation Darknet by the Brazilian Federal Police. This criminal structure has unique criminophysical characteristics such as a small fraction of users responsible for sharing illicit media, and a relationship architecture very resilient to police intervention. The network differs from typical criminal organizations, approaching to some degree the dynamics observed in terrorist cells. It also shows a topology analogous to the structure
of many known biological viruses. Correlation measures such as rich-club and assortativity indicate that there is a cooperation between small and medium-sized criminals, while the most prominent individuals in the network get support from the large number of users who only view the illicit material. Interventions based on High Topological Payoff
Targets indicate that police work could have been 1.6 times more efficient. While structurally wise police intervention was similar to random attacks, it achieved high efficiency by focusing on the viewing network, as only 10 users contributed to more than 1/3 of the total post views and, of these, 8 were arrested by the police.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The journal has exclusive rights over the first publication, printed and/or digital, of this academic text, which does not affect the copyright of the person responsible for the research.
The reproduction (in whole or in part) of the published material depends on the express mention of this journal as the origin, by citing the volume, edition number and the DOI link for cross-reference. For rights purposes, the original publication source must be recorded.
The use of the results published here in other vehicles of scientific divulgation, even if by the authors, depends on the express indication of this journal as a means of original publication, under penalty of characterizing a situation of self-plagiarism.
____________________________________________
Additional information and author statements
(scientific integrity)
Declaration of conflict of interest: The author(s) confirm that there are no conflicts of interest in conducting this research and writing this article.
Authorship statement: All and only researchers who meet the authorship requirements for this article are listed as authors; all co-authors are fully responsible for this work in its entirety.
Declaration of originality: The author(s) guarantee that the text published here has not been previously published elsewhere and that future republication will only be made with express reference to the original place of publication; also certifies that there is no plagiarism of third-party material or self-plagiarism.
____________________________________________
Archiving and distribution
The final published PDF can be archived, without restrictions, on any open access server, indexer, repository or personal page, such as Academia.edu and ResearchGate.
How to Cite
References
ALSTOTT, J. et al. A unifying framework for measuring weighted rich clubs. Scientific reports, v. 4, n. 1, p. 1-6, 2014. Disponível em: https://doi.org/10.1038/srep07258. Acesso em: 12 mai. 2021.
ASACP. [Statistics], 2021. Disponível em: https://www.asacp.org/index.html?content=statistics. Acesso em 12 mai. 2021.
ASLLANI, Malbor et al. Dynamics impose limits to detectability of network structure. New Journal of Physics, v. 22, n. 6, artigo 063037, 2020. Disponível em: https://doi.org/10.1088/1367-2630/ab8ef9. Acesso em: 12 mai. 2021.
BARABÁSI, A.L.Network Science. Cambridge: Cambridge University Press, 2016.
CUNHA, B.R. Estudo sobre a topologia das redes criminais. 2017. 84 f. Tese (Doutorado em Física Teórica) - Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Tese de Doutorado, 2017. Disponível em: https://www.lume.ufrgs.br/handle/10183/169125 . Acesso em: 12 mai. 2021.
CUNHA, B.R. Argumento topológico para a priorização de alvos-chave em organizações criminosas. In: BALDAN, E.L.; FERNANDES, A.P.P. Ciências policiais e segurança pública. 1 ed. Goiânia: Ilumina, 2018. p. 234-236.
CUNHA, B.R. Affronter les factions criminelles et le crime organisé au Brésil: le recours à la science des réseaux. In: DOLO, N.; RACOUCHOT, B. Brésil; corruption, trafic, violence, criminalité; vers la fin du cauchemar? Collection Sécurité Globale. Paris: MA Editions - ESKA, 2018. p. 234-236.
CUNHA, B.R. Criminofísica: a ciência das interações criminais. Editora Buqui, 2021.
CUNHA, B.R. Neutralização Seletiva de Alvos Topológicos de Alto Retorno em Facções Criminosas. Revista Brasileira de Ciências Policiais, v. 12, n. 4, p. 53-73, 2021. Disponível em: http://dx.doi.org/10.31412%2Frbcp.v12i4.616. Acesso em: 12 mai. 2021.
CUNHA, B.R. et al. Assessing police topological efficiency in a major sting operation on the dark web. Scientific reports, v. 10, n. 1, p. 1-10, 2020. Disponível em: https://doi.org/10.1038/s41598-019-56704-4. Acesso em: 12 mai. 2021.
CUNHA, B.R.; GONÇALVES, S. Performance of attack strategies on modular networks. Journal of Complex Networks, Oxford, v. 5, n. 6, p. 913-923, 2017. Disponível em: https://doi.org/10.1093/comnet/cnx015. Acesso em: 12 mai. 2021.
CUNHA, B.R.; GONÇALVES, S. Topology, robustness, and structural controllability of the Brazilian Federal Police criminal intelligence network. Applied Network Science, v. 3, n. 1, p. 36, 2018. Disponível em: https://doi.org/10.1007/s41109-018-0092-1. Acesso em: 12 mai. 2021.
CUNHA, B.R.; GONZÁLEZ-AVELLA, J.C.; GONÇALVES, S. Fast fragmentation of networks using module-based attacks. PloS one, São Francisco, v. 10, n. 11, p. e0142824, 2015. Disponível em: https://doi.org/10.1371/journal.pone.0142824. Acesso em: 12 mai. 2021.
ABREU, C.; GONÇALVES, S.; DA CUNHA, B.R. Empirical determination of the optimal attack for fragmentation of modular networks. Physica A: Statistical Mechanics and its Applications, v. 563, p. 125486, 2021. Disponível em: https://doi.org/10.1016/j.physa.2020.125486. Acesso em: 12 mai. 2021.
DUIJN, P.A.C.; KASHIRIN, V.; SLOOT, P.M.A. The relative ineffectiveness of criminal network disruption. Scientific Reports, v. 4, p. 4238, 2014. Disponível em:https://doi.org/10.1038/srep04238. Acesso em: 12 mai. 2021.
ESTRADA, E.; KNIGHT, P.A. A first course in network theory. Oxford: Oxford University Press, 2015.
GIRVAN, M.; NEWMAN, M.E.J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, Washington, v. 99, n. 12, p. 7821-7826, 2002. Disponível em: https://doi.org/10.1073/pnas.122653799. Acesso em: 12 mai. 2021.
MORSELLI, C. Inside criminal networks. Nova Iorque: Springer, 2009.
NCMEC. [Case resources], 2021. Disponível em: https://www.missingkids.org/ourwork/caseresources. Acesso em: 12 mai. 2021.
OPSAHL, T.; COLIZZA, V.; PANZARASA, P.; RAMASCO, J.J. Prominence and Control: The Weighted Rich-Club Effect. Phys. Rev. Lett. 101, 168702. Disponível em: https://doi.org/10.1103/PhysRevLett.101.168702. Acesso em: 12 mai. 2021.
PAPACHRISTOS, A.V. The coming of a networked criminology? In: MACDONALD, J. Measuring Crime and Criminality: Advances in Criminological Theory. Nova Jersey: Transaction Publishers, 2011. p. 101-140.
POLÍCIA FEDERAL. [Operação Darknet – Balanço]. Brasília, 2014. Disponível em: http://www.pf.gov.br/agencia/noticias/2014/10/operacao-darknet-balanco. Acesso em: 12 mai. 2021.
POLÍCIA FEDERAL. [PF divulga balanço da Operação Darknet II]. Brasília, 2016. Disponível em: http://www.pf.gov.br/agencia/noticias/2016/11/pf-divulga-balanco-da-operacao-darknet-ii. Acesso em: 12 mai. 2021.
SCHNEIDER, E. et al. Dynamic Modular Networks Model Mediated by Confinement. Applied Network Science, v. 4, n. 1, 2019. Disponível em: https://doi.org/10.1007/s41109-019-0143-2. Acesso em: 12 mai. 2021.