Uma proposta de controlador SDN e Aprendizado de Máquina para detecção de ataques por botnets em redes IoT: uma abordagem para o ensino de redes de computadores
Abstract
In the field of computer network education, it is important to use simulated scenarios that resemble real-world environments to enhance learning and knowledge retention. This article describes a proposal for a Software-Defined Networking (SDN) controller that can detect attacks in Internet of Things (IoT) networks. The proposal suggests a high-performance distributed backend with artificial intelligence (AI) using the OpenFlow protocol to quickly intervene in switches connecting sensors and actuators. To validate the proposal, the BotIoT dataset and a simulated environment in GNS3 were used. By combining real attack situations and SDN concepts, this provides a suitable environment for understanding the use of artificial intelligence in computer networks.
References
Corino, M., Bertagnolli, S., and Schmitt, M. (2020). Desenvolvimento e aplicação de uma estratégia pedagógica para o ensino de redes de computadores com robótica educacional. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 1663–1672, Porto Alegre, RS, Brasil. SBC.
Costa, V., Zarpelão, B., Miani, R., and Junior, S. B. (2018). Online detection of botnets on network flows using stream mining. In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 225–238, Porto Alegre, RS, Brasil. SBC.
Duan, L., Zhou, J., Wu, Y., and Xu, W. (2022). A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems. International Journal of Distributed Sensor Networks, 18(3):15501477211049910.
Gil, P., García, G. J., Delgado, A. D., Medina, R. M., Calderon, A., and Marti, P. (2014). Computer networks virtualization with GNS3: evaluating a solution to optimize resources and achieve a distance learning. In IEEE Frontiers in Education Conference, FIE 2014, Proceedings, Madrid, Spain, October 22-25, 2014, pages 1–4. IEEE Computer Society.
Kh, D. R., Botirov, S., and Juraev, F. (2021). A simulation model of a cloud data center based on traditional networks and software-defined network. In 2021 International Conference on Information Science and Communications Technologies (ICISCT), pages 1–4. IEEE.
Li, M., Yu, F. R., Si, P., and Zhang, Y. (2019). Energy-efficient machine-to-machine (m2m) communications in virtualized cellular networks with mobile edge computing (mec). IEEE Transactions on Mobile Computing, 18(7):1541–1555.
Maeda, S., Kanai, A., Tanimoto, S., Hatashima, T., and Ohkubo, K. (2019). A botnet detection method on sdn using deep learning. In 2019 IEEE International Conference on Consumer Electronics (ICCE), pages 1–6.
Nunez, A., Ayoka, J., Islam, M. Z., and Ruiz, P. (2023). A brief overview of software-defined networking.
Yamasaki, Y., Miyamoto, Y., Yamato, J., Goto, H., and Sone, H. (2011). Flexible access management system for campus vlan based on openflow. In Proceedings - 11th IEEE/IPSJ International Symposium on Applications and the Internet, SAINT 2011, Proceedings - 11th IEEE/IPSJ International Symposium on Applications and the Internet, SAINT 2011, pages 347–351. 11th IEEE/IPSJ International Symposium on Applications and the Internet, SAINT 2011 ; Conference date: 18-07-2011 Through 21-07-2011.
Costa, V., Zarpelão, B., Miani, R., and Junior, S. B. (2018). Online detection of botnets on network flows using stream mining. In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 225–238, Porto Alegre, RS, Brasil. SBC.
Duan, L., Zhou, J., Wu, Y., and Xu, W. (2022). A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems. International Journal of Distributed Sensor Networks, 18(3):15501477211049910.
Gil, P., García, G. J., Delgado, A. D., Medina, R. M., Calderon, A., and Marti, P. (2014). Computer networks virtualization with GNS3: evaluating a solution to optimize resources and achieve a distance learning. In IEEE Frontiers in Education Conference, FIE 2014, Proceedings, Madrid, Spain, October 22-25, 2014, pages 1–4. IEEE Computer Society.
Kh, D. R., Botirov, S., and Juraev, F. (2021). A simulation model of a cloud data center based on traditional networks and software-defined network. In 2021 International Conference on Information Science and Communications Technologies (ICISCT), pages 1–4. IEEE.
Li, M., Yu, F. R., Si, P., and Zhang, Y. (2019). Energy-efficient machine-to-machine (m2m) communications in virtualized cellular networks with mobile edge computing (mec). IEEE Transactions on Mobile Computing, 18(7):1541–1555.
Maeda, S., Kanai, A., Tanimoto, S., Hatashima, T., and Ohkubo, K. (2019). A botnet detection method on sdn using deep learning. In 2019 IEEE International Conference on Consumer Electronics (ICCE), pages 1–6.
Nunez, A., Ayoka, J., Islam, M. Z., and Ruiz, P. (2023). A brief overview of software-defined networking.
Yamasaki, Y., Miyamoto, Y., Yamato, J., Goto, H., and Sone, H. (2011). Flexible access management system for campus vlan based on openflow. In Proceedings - 11th IEEE/IPSJ International Symposium on Applications and the Internet, SAINT 2011, Proceedings - 11th IEEE/IPSJ International Symposium on Applications and the Internet, SAINT 2011, pages 347–351. 11th IEEE/IPSJ International Symposium on Applications and the Internet, SAINT 2011 ; Conference date: 18-07-2011 Through 21-07-2011.
Published
2023-08-23
How to Cite
COSTA, Bruno Henrique Graziano; RODRIGUES, Antonio Wendell de Oliveira.
Uma proposta de controlador SDN e Aprendizado de Máquina para detecção de ataques por botnets em redes IoT: uma abordagem para o ensino de redes de computadores. In: CONGRESS ON TECHNOLOGIES IN EDUCATION (CTRL+E), 8. , 2023, Santarém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 463-468.
DOI: https://doi.org/10.5753/ctrle.2023.232727.
