Segurança de Dados em Nuvem através de Aprendizado de Máquina: uma Revisão Sistemática da Literatura
Resumo
O presente trabalho tem como objetivo agregar, comparar e sintetizar (por meio de umarevisão sistemática) os trabalhos presentes na literatura, que utilizam aprendizado de máquina para lidar com ameaças de segurança, no contexto da computação em nuvem. Como principais resultados, sintetizou-se uma base de conhecimento e foram observadas demandas que indicam possíveis questões relevantes de pesquisa, e, consequentemente, estudos mais aprofundados, como: a investigação da Security as a Service (SecaaS).
Palavras-chave:
Segurança de dados, nuvem, aprendizagem de máquina
Referências
Abdalla, G., Damasceno, C. D. N., and Nakagawa, E. Y. (2015). A systematic literature review on systems-of-systems knowledge representation. Inst. Math. Comput. Sci., Univ. Sao Paulo, Sao Carlos, Brazil, Tech. Rep.
Al Haddad, Z., Hanoune, M., and Mamouni, A. (2016). A collaborative framework for intrusion detection (c-nids) in cloud computing. In 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech), pages 261–265. IEEE.
Ashktorab, V., Taghizadeh, S. R., et al. (2012). Security threats and countermeasures in cloud computing. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 1(2):234–245.
Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., and Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of systems and software, 80(4):571–583.
Cui, B. and He, S. (2016). Anomaly detection model based on hadoop platform and weka interface. In 2016 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pages 84–89. IEEE.
Ghosh, P. and Mitra, R. (2015). Proposed ga-bfss and logistic regression based intrusion detection system. In Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pages 1–6. IEEE.
Huang, C., Min, G., Wu, Y., Ying, Y., Pei, K., and Xiang, Z. (2017). Time series anomaly detection for trustworthy services in cloud computing systems. IEEE Transactions on Big Data.
Idhammad, M., Afdel, K., and Belouch, M. (2018). Distributed intrusion detection system for cloud environments based on data mining techniques. Procedia Computer Science, 127:35–41.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, Ver. 2.3 EBSE Technical Report. EBSE.
Kim, H., Kim, J., Kim, Y., Kim, I., and Kim, K. J. (2019). Design of network threat detection and classification based on machine learning on cloud computing. Cluster Computing, 22(1):2341–2350.
Kumar, R. S. S., Wicker, A., and Swann, M. (2017). Practical machine learning for cloud intrusion detection: challenges and the way forward. In Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pages 81–90.
Kumara, A. and Jaidhar, C. (2018). Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at vmm. Future Generation Computer Systems, 79:431–446.
Lin, A. and Chen, N.-C. (2012). Cloud computing as an innovation: Percepetion, attitude, and adoption. International Journal of Information Management, 32(6):533–540.
Lipton, Z. C., Berkowitz, J., and Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
Masetic, Z., Hajdarevic, K., and Dogru, N. (2017). Cloud computing threats classification model based on the detection feasibility of machine learning algorithms. In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1314–1318. IEEE.
Mell, P., Grance, T., et al. (2011). The nist definition of cloud computing. National Institute of Science and Technology Cloud, Hybrid Special Publication.
Mishra, P., Varadharajan, V., Tupakula, U., and Pilli, E. S. (2018). A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Communications Surveys & Tutorials, 21(1):686–728.
Modi, C., Patel, D., Borisaniya, B., Patel, A., and Rajarajan, M. (2013). A survey on security issues and solutions at different layers of cloud computing. The journal of supercomputing, 63(2):561–592.
Moustafa, N., Choo, K.-K. R., Radwan, I., and Camtepe, S. (2019a). Outlier dirichlet mixture mechanism: Adversarial statistical learning for anomaly detection in the fog. IEEE Transactions on Information Forensics and Security, 14(8):1975–1987.
Moustafa, N., Hu, J., and Slay, J. (2019b). A holistic review of network anomaly detection systems: A comprehensive survey. Journal of Network and Computer Applications, 128:33–55.
Patil, G. R. M. D. A. (2018). Data breaches as top security concern in cloud computing. International Journal of Pure and Applied Mathematics, 119(14):19–28.
Pereira, A. L., Penha, E. W. M., Gomes, N. A., and Freitas, R. R. (2016). Computação em nuvem: a segurança da informação em ambientes na nuvem e em redes físicas. Brazilian Journal of Production Engineering-BJPE, 2(1):12–27.
Subramanian, N. and Jeyaraj, A. (2018). Recent security challenges in cloud computing. Computers & Electrical Engineering, 71:28–42.
Tan, Z., Nagar, U. T., He, X., Nanda, P., Liu, R. P., Wang, S., and Hu, J. (2014). Enhancing big data security with collaborative intrusion detection. IEEE cloud computing, 1(3):27–33.
Win, T. Y., Tianfield, H., and Mair, Q. (2017). Big data based security analytics for protecting virtualized infrastructures in cloud computing. IEEE Transactions on Big Data, 4(1):11–25.
Zekri, M., El Kafhali, S., Aboutabit, N., and Saadi, Y. (2017). Ddos attack detection using machine learning techniques in cloud computing environments. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), pages 1–7. IEEE.
Al Haddad, Z., Hanoune, M., and Mamouni, A. (2016). A collaborative framework for intrusion detection (c-nids) in cloud computing. In 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech), pages 261–265. IEEE.
Ashktorab, V., Taghizadeh, S. R., et al. (2012). Security threats and countermeasures in cloud computing. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 1(2):234–245.
Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., and Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of systems and software, 80(4):571–583.
Cui, B. and He, S. (2016). Anomaly detection model based on hadoop platform and weka interface. In 2016 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pages 84–89. IEEE.
Ghosh, P. and Mitra, R. (2015). Proposed ga-bfss and logistic regression based intrusion detection system. In Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pages 1–6. IEEE.
Huang, C., Min, G., Wu, Y., Ying, Y., Pei, K., and Xiang, Z. (2017). Time series anomaly detection for trustworthy services in cloud computing systems. IEEE Transactions on Big Data.
Idhammad, M., Afdel, K., and Belouch, M. (2018). Distributed intrusion detection system for cloud environments based on data mining techniques. Procedia Computer Science, 127:35–41.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, Ver. 2.3 EBSE Technical Report. EBSE.
Kim, H., Kim, J., Kim, Y., Kim, I., and Kim, K. J. (2019). Design of network threat detection and classification based on machine learning on cloud computing. Cluster Computing, 22(1):2341–2350.
Kumar, R. S. S., Wicker, A., and Swann, M. (2017). Practical machine learning for cloud intrusion detection: challenges and the way forward. In Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pages 81–90.
Kumara, A. and Jaidhar, C. (2018). Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at vmm. Future Generation Computer Systems, 79:431–446.
Lin, A. and Chen, N.-C. (2012). Cloud computing as an innovation: Percepetion, attitude, and adoption. International Journal of Information Management, 32(6):533–540.
Lipton, Z. C., Berkowitz, J., and Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
Masetic, Z., Hajdarevic, K., and Dogru, N. (2017). Cloud computing threats classification model based on the detection feasibility of machine learning algorithms. In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1314–1318. IEEE.
Mell, P., Grance, T., et al. (2011). The nist definition of cloud computing. National Institute of Science and Technology Cloud, Hybrid Special Publication.
Mishra, P., Varadharajan, V., Tupakula, U., and Pilli, E. S. (2018). A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Communications Surveys & Tutorials, 21(1):686–728.
Modi, C., Patel, D., Borisaniya, B., Patel, A., and Rajarajan, M. (2013). A survey on security issues and solutions at different layers of cloud computing. The journal of supercomputing, 63(2):561–592.
Moustafa, N., Choo, K.-K. R., Radwan, I., and Camtepe, S. (2019a). Outlier dirichlet mixture mechanism: Adversarial statistical learning for anomaly detection in the fog. IEEE Transactions on Information Forensics and Security, 14(8):1975–1987.
Moustafa, N., Hu, J., and Slay, J. (2019b). A holistic review of network anomaly detection systems: A comprehensive survey. Journal of Network and Computer Applications, 128:33–55.
Patil, G. R. M. D. A. (2018). Data breaches as top security concern in cloud computing. International Journal of Pure and Applied Mathematics, 119(14):19–28.
Pereira, A. L., Penha, E. W. M., Gomes, N. A., and Freitas, R. R. (2016). Computação em nuvem: a segurança da informação em ambientes na nuvem e em redes físicas. Brazilian Journal of Production Engineering-BJPE, 2(1):12–27.
Subramanian, N. and Jeyaraj, A. (2018). Recent security challenges in cloud computing. Computers & Electrical Engineering, 71:28–42.
Tan, Z., Nagar, U. T., He, X., Nanda, P., Liu, R. P., Wang, S., and Hu, J. (2014). Enhancing big data security with collaborative intrusion detection. IEEE cloud computing, 1(3):27–33.
Win, T. Y., Tianfield, H., and Mair, Q. (2017). Big data based security analytics for protecting virtualized infrastructures in cloud computing. IEEE Transactions on Big Data, 4(1):11–25.
Zekri, M., El Kafhali, S., Aboutabit, N., and Saadi, Y. (2017). Ddos attack detection using machine learning techniques in cloud computing environments. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), pages 1–7. IEEE.
Publicado
03/11/2020
Como Citar
LACERDA, Matheus; FEITOSA, Robson.
Segurança de Dados em Nuvem através de Aprendizado de Máquina: uma Revisão Sistemática da Literatura. In: CONCURSO DE TRABALHOS DE CONCLUSÃO DE CURSO EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 185-194.
DOI: https://doi.org/10.5753/sbsi.2020.13140.
