Detecção de Fraudes na Emissão de Certificados Digitais dentro da Infraestrutura de Chaves Públicas Brasileira
Resumo
Muitas das interações com os sistemas de governo eletrônico (e-gov) no Brasil são realizadas por meio do uso de certificados digitais emitidos por uma infraestrutura de chaves públicas controlada pelo governo (ICP-Brasil). Os certificados digitais são uma ferramenta perfeita para que pessoas se passarem por outras pessoas virtualmente, roubando assim sua identidade. A fraude na emissão de um certificado digital pode abrir portas para autenticação em sistemas e acesso a documentos sigilosos. O processo atual é manual, custoso, está sujeito a falhas e corrupção humana. Visto isso, este trabalho propõe a automatização do processo de detecção de fraudes através da proposta de uma abordagem híbrida de aprendizado de máquina utilizando uma técnica de clusterização baseada no DBSCAN e classificação usando os dados utilizados no processo de emissão de certificados digitais da ICP-Brasil. Esse sistema de detecção de fraude torna o sistema ICP-Brasil mais seguro, mais rápido e mais barato.
Referências
Abdelhalim, A. and Traoré, I. (2010). Unsupervised identity application fraud detection using rule-based decision tree. In SEDE, pages 261–268.
Agrawal, S. and Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60:708–713.
Amazon (2020). Detecte, analise e compare rostos.
Awoyemi, J. O., Adetunmbi, A. O., and Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. In 2017 International Conference on Computing Networking and Informatics (ICCNI), pages 1–9. IEEE.
Bolton, R. J. and Hand, D. J. (2002). Statistical fraud detection: A review. Statistical science, pages 235–249.
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):1–58.
da Costa, K. A., Papa, J. P., Lisboa, C. O., Munoz, R., and de Albuquerque, V. H. C. (2019). Internet of things: A survey on machine learning-based intrusion detection approaches. Computer Networks, 151:147–157.
Dutta, S., Gupta, A. K., and Narayan, N. (2017). Identity crime detection using data mining. In 2017 3rd International Conference on Computational Intelligence and Networks (CINE), pages 1–5. IEEE.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226–231.
Han, J., Kamber, M., and Pei, J. (2011). Data mining concepts and techniques third edition. Morgan Kaufmann.
ITI (2020a). Instituto Nacional de Tecnologia da Informação.
ITI (2020b). Procedimento de Identificação do Requerente.
Kou, Y., Lu, C.-T., Sirwongwattana, S., and Huang, Y.-P. (2004). Survey of fraud detection techniques. In IEEE International Conference on Networking, Sensing and Control, 2004, volume 2, pages 749–754. IEEE.
Kshirsagar, A. and Dole, L. (2014). Recognizing the theft of identity using data mining. International Journal of Emerging Technology and Advanced Engineering (ISSN 22502459, ISO 9001: 2008 Certified Journal, Volume 4, Issue 4.
Lee, W. and Stolfo, S. (1998). Data mining approaches for intrusion detection.
Lee, W., Stolfo, S. J., and Mok, K. W. (2000). Adaptive intrusion detection: A data mining approach. Artificial Intelligence Review, 14(6):533–567.
Padhi, B., Chakravarty, S., and Biswal, B. (2020). Anonymized credit card transaction using machine learning techniques. In Advances in Intelligent Computing and Communication, pages 413–423. Springer.
Phua, C., Gayler, R., Lee, V., and Smith-Miles, K. (2009). On the communal analysis suspicion scoring for identity crime in streaming credit applications. European Journal of Operational Research, 195(2):595–612.
Phua, C., Lee, V., Smith, K., and Gayler, R. (2010a). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Phua, C., Smith-Miles, K., Lee, V., and Gayler, R. (2010b). Resilient identity crime detection. IEEE transactions on knowledge and data engineering, 24(3):533–546.
Sproule, S. and Archer, N. (2007). Defining identity theft. In Eighth World Congress on the Management of eBusiness (WCMeB 2007), pages 20–20. IEEE.
Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., and Kuruwitaarachchi, N. In 2019 9th (2019). Real-time credit card fraud detection using machine learning. International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 488–493. IEEE.
Wheeler, R. and Aitken, S. (2000). Multiple algorithms for fraud detection. In Applications and Innovations in Intelligent Systems VII, pages 219–231. Springer.
Zhao, Q., Chen, K., Li, T., Yang, Y., and Wang, X. (2018). Detecting telecommunication fraud by understanding the contents of a call. Cybersecurity, 1(1):8.
Agrawal, S. and Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60:708–713.
Amazon (2020). Detecte, analise e compare rostos.
Awoyemi, J. O., Adetunmbi, A. O., and Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. In 2017 International Conference on Computing Networking and Informatics (ICCNI), pages 1–9. IEEE.
Bolton, R. J. and Hand, D. J. (2002). Statistical fraud detection: A review. Statistical science, pages 235–249.
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):1–58.
da Costa, K. A., Papa, J. P., Lisboa, C. O., Munoz, R., and de Albuquerque, V. H. C. (2019). Internet of things: A survey on machine learning-based intrusion detection approaches. Computer Networks, 151:147–157.
Dutta, S., Gupta, A. K., and Narayan, N. (2017). Identity crime detection using data mining. In 2017 3rd International Conference on Computational Intelligence and Networks (CINE), pages 1–5. IEEE.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226–231.
Han, J., Kamber, M., and Pei, J. (2011). Data mining concepts and techniques third edition. Morgan Kaufmann.
ITI (2020a). Instituto Nacional de Tecnologia da Informação.
ITI (2020b). Procedimento de Identificação do Requerente.
Kou, Y., Lu, C.-T., Sirwongwattana, S., and Huang, Y.-P. (2004). Survey of fraud detection techniques. In IEEE International Conference on Networking, Sensing and Control, 2004, volume 2, pages 749–754. IEEE.
Kshirsagar, A. and Dole, L. (2014). Recognizing the theft of identity using data mining. International Journal of Emerging Technology and Advanced Engineering (ISSN 22502459, ISO 9001: 2008 Certified Journal, Volume 4, Issue 4.
Lee, W. and Stolfo, S. (1998). Data mining approaches for intrusion detection.
Lee, W., Stolfo, S. J., and Mok, K. W. (2000). Adaptive intrusion detection: A data mining approach. Artificial Intelligence Review, 14(6):533–567.
Padhi, B., Chakravarty, S., and Biswal, B. (2020). Anonymized credit card transaction using machine learning techniques. In Advances in Intelligent Computing and Communication, pages 413–423. Springer.
Phua, C., Gayler, R., Lee, V., and Smith-Miles, K. (2009). On the communal analysis suspicion scoring for identity crime in streaming credit applications. European Journal of Operational Research, 195(2):595–612.
Phua, C., Lee, V., Smith, K., and Gayler, R. (2010a). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Phua, C., Smith-Miles, K., Lee, V., and Gayler, R. (2010b). Resilient identity crime detection. IEEE transactions on knowledge and data engineering, 24(3):533–546.
Sproule, S. and Archer, N. (2007). Defining identity theft. In Eighth World Congress on the Management of eBusiness (WCMeB 2007), pages 20–20. IEEE.
Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., and Kuruwitaarachchi, N. In 2019 9th (2019). Real-time credit card fraud detection using machine learning. International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 488–493. IEEE.
Wheeler, R. and Aitken, S. (2000). Multiple algorithms for fraud detection. In Applications and Innovations in Intelligent Systems VII, pages 219–231. Springer.
Zhao, Q., Chen, K., Li, T., Yang, Y., and Wang, X. (2018). Detecting telecommunication fraud by understanding the contents of a call. Cybersecurity, 1(1):8.
Publicado
13/10/2020
Como Citar
GOMES, Fernanda O.; AGOSTINHO, Bruno M.; BALDISERA, Julia; SILVEIRA, Raphael S. da; MARTINA, Jean E..
Detecção de Fraudes na Emissão de Certificados Digitais dentro da Infraestrutura de Chaves Públicas Brasileira. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 20. , 2020, Petrópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 215-228.
DOI: https://doi.org/10.5753/sbseg.2020.19239.
