Crimes Cibernéticos no Piauí: Uma Análise Estatística com CRISP-DM e Dados Oficiais (2019–2022)
Resumo
Este estudo analisa crimes cibernéticos registrados no Piauíentre 2019 e 2022, com base em dados oficiais da Secretaria de Segurança Pública e na metodologia CRISP-DM. Após o tratamento e categorização com bibliotecas Python (Pandas, Seaborn e Matplotlib), aplicaram-se análises estatísticas e visuais para identificar padrões. Os crimes mais recorrentes foram estelionato digital, invasão de dispositivos e fraudes, com maior incidência entre adultos de 25 a 45 anos. Estudantes, autônomos e aposentados figuram entre os grupos mais vulneráveis. O estudo destaca desigualdades digitais e propõe o uso de modelos preditivos e políticas públicas orientadas por dados para fortalecer a segurança da informação.
Referências
Provost, Foster; Fawcett, Tom. (2013). Data science for business. Sebastopol: O’Reilly Media.
Han, Jiawei; Kamber, Micheline; Pei, Jian. (2012). Data mining: concepts and techniques. 3rd ed. San Francisco: Morgan Kaufmann.
Witten, Ian H.; Frank, Eibe; Hall, Mark A. (2011). Data mining: practical machine learning tools and techniques. 3rd ed. San Francisco: Morgan Kaufmann.
Kelleher, John D.; Mac Namee, Brian; D’Arcy, Aoife. (2015). Fundamentals of machine learning for predictive data analytics. Cambridge: MIT Press.
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer.
Bishop, Christopher M. (2006). Pattern recognition and machine learning. New York: Springer.
Dean, Jeffrey; Ghemawat, Sanjay. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1):107–113.
Murphy, Kevin P. (2012). Machine learning: a probabilistic perspective. Cambridge: MIT Press.
McKinney, Wes. (2018). Python for data analysis. 2nd ed. Sebastopol: O’Reilly Media.
Hunter, John D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3):90–95.
Waskom, Michael L. (2021). Seaborn: Statistical data visualization. Journal of Open Source Software, 6(60).
Box, George E. P.; Draper, Norman R. (1987). Empirical model-building and response surfaces. New York: Wiley.
Rubinstein, Reuven Y.; Kroese, Dirk P. (2016). Simulation and the Monte Carlo method. 3rd ed. New York: Wiley.
Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3):37–54.
Baesens, Bart. (2014). Analytics in a big data world: the essential guide to data science and its applications. Hoboken: Wiley.
Anderson, Ross; Moore, Tyler; Acquisti, Alessandro, et al. (2009). Information security economics—and beyond. In: Acquisti, A. et al. (Eds.). Economics of information security and privacy. New York: Springer, p. 17–44.
Han, Jiawei; Kamber, Micheline; Pei, Jian. (2012). Data mining: concepts and techniques. 3rd ed. San Francisco: Morgan Kaufmann.
Witten, Ian H.; Frank, Eibe; Hall, Mark A. (2011). Data mining: practical machine learning tools and techniques. 3rd ed. San Francisco: Morgan Kaufmann.
Kelleher, John D.; Mac Namee, Brian; D’Arcy, Aoife. (2015). Fundamentals of machine learning for predictive data analytics. Cambridge: MIT Press.
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer.
Bishop, Christopher M. (2006). Pattern recognition and machine learning. New York: Springer.
Dean, Jeffrey; Ghemawat, Sanjay. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1):107–113.
Murphy, Kevin P. (2012). Machine learning: a probabilistic perspective. Cambridge: MIT Press.
McKinney, Wes. (2018). Python for data analysis. 2nd ed. Sebastopol: O’Reilly Media.
Hunter, John D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3):90–95.
Waskom, Michael L. (2021). Seaborn: Statistical data visualization. Journal of Open Source Software, 6(60).
Box, George E. P.; Draper, Norman R. (1987). Empirical model-building and response surfaces. New York: Wiley.
Rubinstein, Reuven Y.; Kroese, Dirk P. (2016). Simulation and the Monte Carlo method. 3rd ed. New York: Wiley.
Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3):37–54.
Baesens, Bart. (2014). Analytics in a big data world: the essential guide to data science and its applications. Hoboken: Wiley.
Anderson, Ross; Moore, Tyler; Acquisti, Alessandro, et al. (2009). Information security economics—and beyond. In: Acquisti, A. et al. (Eds.). Economics of information security and privacy. New York: Springer, p. 17–44.
Publicado
28/05/2025
Como Citar
LEITE, Gabriel Linard; FONSECA, João Paulo de Sá; REIS, Matheus Aquino Torquato; SANTOS, Carlos Eduardo; ALVES, Adler Castro; ABREU, Maria Hellem Teixeira.
Crimes Cibernéticos no Piauí: Uma Análise Estatística com CRISP-DM e Dados Oficiais (2019–2022). In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 17. , 2025, Teresina/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 179-187.
DOI: https://doi.org/10.5753/enucompi.2025.9795.
