Resumo de Grandes Volumes de Dados com Filtro de Bloom: Uma Abordagem Eficiente para Aprendizado Profundo com Redes Neurais Convolucionais em Fluxos de Rede
Resumo
Este artigo propõe a aplicação de filtros de Bloom para a geração de resumos de dados bidimensionais a partir de fluxos em uma janela de uso da rede formando um mapa de bits. Após a geração dos resumos, o artigo aplica o aprendizado profundo, composto por camadas de rede neural convolucional, para a segmentação do mapa de bits. A segmentação do mapa de bits é uma tarefa da visão computacional que é eficientemente provida por redes neurais convolucionais. As principais contribuições do artigo são (i) a proposta de uma técnica de resumo bidimensional de dados em uma janela de fluxos através de filtros de Bloom; (ii) a aplicação do aprendizado profundo com redes neurais convolucionais em fluxos de redes e (iii) a execução otimizada da proposta em unidades de processamento gráfico (GPU). A proposta é avaliada sobre um conjunto de dados real de um provedor de acesso de banda larga e os resultados demonstram a eficiência dos filtros usados e a precisão superior a 0,90 do aprendizado profundo com treinamento incremental.
Referências
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Viegas, E., Santin, A., Bessani, A. e Neves, N. (2019). Bigow: Real-time and reliable anomaly-based intrusion detection for high-speed networks. Future Generation Computer Systems, 93:473 – 485.
Wang, Z. e Majewicz Fey, A. (2018). Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. International Journal of Computer Assisted Radiology and Surgery, 13(12):1959–1970.
Xiao, P., Li, Z., Qi, H., Qu, W. e Yu, H. (2016). An efficient ddos detection with bloom filter in sdn. Em 2016 IEEE Trustcom/BigDataSE/ISPA, p. 1–6. IEEE.
Zhang, Y., Chen, X., Guo, D., Song, M., Teng, Y. e Wang, X. (2019). Pccn: Parallel cross convolutional neural network for abnormal network traffic ows detection in multiclass imbalanced network traffic ows. IEEE Access, 7:119904–119916.
Andreoni Lopez, M., Sanz, I. J., Lobato, A. G. P., Mattos, D. M. F. e Duarte, O. C. M. B. (2018). Aprendizado de máquina em plataformas de processamento distribuído de uxo: Análise e detecção de ameaças em tempo real. Minicursos do Simpósio Brasileiro de Redes de Computadores-SBRC, 2018:150–206.
Andreoni Lopez, M., Silva, S. R., Alvarenga, I. D., Rebello, G. A. F., Sanz, J. I., Lobato, A. G. P., Mattos, D. M. F., Duarte, O. C. M. B. e Pujolle, G. (2017). Collecting and Characterizing a Real Broadband Access Network Traffic Dataset. Em IEEE/IFIP 1st Cyber Security in Networking Conference (CSNet’17), p. 1–8, Brazil.
Casas, P., Soro, F., Vanerio, J., Settanni, G. e D’Alconzo, A. (2017). Network security and anomaly detection with big-dama, a big data analytics framework. Em 2017 IEEE 6th International Conference on Cloud Networking (CloudNet), p. 1–7.
de Oliveira, M. T., Reis, L. H. A., Medeiros, D. S. V., Carrano, R. C., Olabarriaga, S. D. e Mattos, D. M. F. (2020). Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications. Comput. Networks, 179:107367.
de Oliveira, N. R., Pisa, P. S., Lopez, M. A., de Medeiros, D. S. V. e Mattos, D. M. F. (2021). Identifying fake news on social networks based on natural language processing: Trends and challenges. Information, 12(1).
de Souza, L. A. C., Antonio F. Rebello, G., Camilo, G. F., Guimarães, L. C. B. e Duarte, O. C. M. B. (2020). Dfedforest: Decentralized federated forest. Em 2020 IEEE International Conference on Blockchain (Blockchain), p. 90–97.
Doriguzzi-Corin, R., Millar, S., Scott-Hayward, S., Martínez-del-Rincón, J. e Siracusa, D. (2020). Lucid: A practical, lightweight deep learning solution for ddos attack detection. IEEE Transactions on Network and Service Management, 17(2):876–889.
Hwang, R.-H., Peng, M.-C., Huang, C.-W., Lin, P.-C. e Nguyen, V.-L. (2020). An unIEEE supervised deep learning model for early network traffic anomaly detection. Access, 8:30387–30399.
Jo, W., Kim, S., Lee, C. e Shon, T. (2020). Packet preprocessing in cnn-based network intrusion detection system. Electronics, 9(7):1151.
Kato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T. e Mizutani, K. (2017). The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective. IEEE Wireless Communications, PP(99):2–9.
Kwon, D., Natarajan, K., Suh, S. C., Kim, H. e Kim, J. (2018). An empirical study on network anomaly detection using convolutional neural networks. Em 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), p. 1595–1598. IEEE.
Laufer, R. P., Velloso, P. B. e Duarte, O. C. M. B. (2011). A generalized bloom filter to secure distributed network applications. Computer Networks, 55(8):1804–1819.
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y. e Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234:11–26.
Lobato, A. G. P., Andreoni Lopez, M., Sanz, I. J., Cardenas, A. A., Duarte, O. C. M. B. e Pujolle, G. (2018). An adaptive real-time architecture for zero-day threat detection. Em 2018 IEEE International Conference on Communications (ICC), p. 1–6.
Qin, Y., Shen, G.-w., Zhao, W.-b., Chen, Y.-p., Yu, M. e Jin, X. (2019). A network security entity recognition method based on feature template and cnn-bilstm-crf. Frontiers of Information Technology & Electronic Engineering, 20(6):872–884.
Reis, L. H. A., Murillo Piedrahita, A., Rueda, S., Fernandes, N. C., Medeiros, D. S. V., de Amorim, M. D. e Mattos, D. M. F. (2020). Unsupervised and incremental learning orchestration for cyber-physical security. Transactions on Emerging Telecommunications Technologies, 31(7):e4011.
Silva, J. V. V., Andreoni Lopez, M. e Mattos, D. M. F. (2020). Attackers are not stealthy: Statistical analysis of the well-known and infamous kdd network security dataset. Em 2020 4th Conference on Cloud and Internet of Things (CIoT), p. 1–8.
Sultana, F., Sufian, A. e Dutta, P. (2020). Evolution of image segmentation using deep convolutional neural network: A survey. Knowledge-Based Systems, 201-202:106062.
Viegas, E., Santin, A., Bessani, A. e Neves, N. (2019). Bigow: Real-time and reliable anomaly-based intrusion detection for high-speed networks. Future Generation Computer Systems, 93:473 – 485.
Wang, Z. e Majewicz Fey, A. (2018). Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. International Journal of Computer Assisted Radiology and Surgery, 13(12):1959–1970.
Xiao, P., Li, Z., Qi, H., Qu, W. e Yu, H. (2016). An efficient ddos detection with bloom filter in sdn. Em 2016 IEEE Trustcom/BigDataSE/ISPA, p. 1–6. IEEE.
Zhang, Y., Chen, X., Guo, D., Song, M., Teng, Y. e Wang, X. (2019). Pccn: Parallel cross convolutional neural network for abnormal network traffic ows detection in multiclass imbalanced network traffic ows. IEEE Access, 7:119904–119916.
Publicado
16/08/2021
Como Citar
LOPEZ, Martin Andreoni; MATTOS, Diogo M. F..
Resumo de Grandes Volumes de Dados com Filtro de Bloom: Uma Abordagem Eficiente para Aprendizado Profundo com Redes Neurais Convolucionais em Fluxos de Rede. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 532-545.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16745.
