Uma abordagem de detecção de atividade maliciosa em ambientes hospitalares
Resumo
A complexidade do ambiente hospitalar e os recursos escassos constituem desafios enfrentados pelos gestores de TI e de segurança cibernética. O setor da Saúde vem recebendo cada vez mais atenção, sofrendo com atividades maliciosas que levam a violação de dados sensíveis. Este artigo apresenta uma abordagem para detectar atividade maliciosa em ambiente hospitalar por meio de classificador baseado em Árvore de Decisão. O modelo foi treinado com dados de simulações executadas a partir da dinâmica de sistemas de ambiente hospitalar. Foram conduzidos experimentos de avaliação, utilizando o f1-score como métrica, com resultados satisfatórios onde o classificador tende a convergir a partir de 100 réplicas de simulação.
Referências
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Forrester, J. W. (1993). System dynamics and the lessons of 35 years. In A systems-based approach to policymaking, pages 199–240. Springer.
Fuentes, M. R. and Huq, N. (2018). Securing connected hospitals. https://documents.trendmicro.com/assets/rpt/rpt-securing-connected-hospitals.pdf, accessed: 10-June-2019.
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1-3):389–422.
Jalali, M. S. and Kaiser, J. P. (2018). Cybersecurity in hospitals: a systematic, organizational perspective. Journal of medical Internet research, 20(5):e10059.
Lelis, C. A. S., de Oliveira Filho, S. R. A., and Marcondes, C. A. C. (2020). Towards hospital dynamics model in the age of cybercrime. In 17th International Conference on Information Technology–New Generations (ITNG 2020), pages 469–475. Springer.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pham, H., Tran, T., and Nakashima, Y. (2019). A secure remote healthcare system for hospital using blockchain smart contract. 2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings.
Ponemon, L. (2019). What’s new in the 2019 cost of a data breach report. https://securityintelligence.com/posts/whats-new-in-the-2019-cost-of-a-databreach-report/ , accessed 10 June 2019.
Steinberg, D. and Colla, P. (2009). Cart: classification and regression trees. The top ten algorithms in data mining, 9:179.
Basili, V. R. and Weiss, D. M. (1984). A methodology for collecting valid software engineering data. IEEE Transactions on software engineering, (6):728–738.
Camiletti, G. G. and Ferracioli, L. (2002). The use of semiquantitative computational modelling in the study of predator-pray system. X International Organization for Science and Technology Education, 2002, Foz do Iguaçu.
Forrester, J. W. (1993). System dynamics and the lessons of 35 years. In A systems-based approach to policymaking, pages 199–240. Springer.
Fuentes, M. R. and Huq, N. (2018). Securing connected hospitals. https://documents.trendmicro.com/assets/rpt/rpt-securing-connected-hospitals.pdf, accessed: 10-June-2019.
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1-3):389–422.
Jalali, M. S. and Kaiser, J. P. (2018). Cybersecurity in hospitals: a systematic, organizational perspective. Journal of medical Internet research, 20(5):e10059.
Lelis, C. A. S., de Oliveira Filho, S. R. A., and Marcondes, C. A. C. (2020). Towards hospital dynamics model in the age of cybercrime. In 17th International Conference on Information Technology–New Generations (ITNG 2020), pages 469–475. Springer.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pham, H., Tran, T., and Nakashima, Y. (2019). A secure remote healthcare system for hospital using blockchain smart contract. 2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings.
Ponemon, L. (2019). What’s new in the 2019 cost of a data breach report. https://securityintelligence.com/posts/whats-new-in-the-2019-cost-of-a-databreach-report/ , accessed 10 June 2019.
Steinberg, D. and Colla, P. (2009). Cart: classification and regression trees. The top ten algorithms in data mining, 9:179.
Publicado
15/09/2020
Como Citar
LELIS, Claudio; PEREIRA, Lourenço; MARCONDES, Cesar.
Uma abordagem de detecção de atividade maliciosa em ambientes hospitalares. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 380-391.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11529.
