MARIA: Monitoramento e Análise para Resposta Imediata a Ataques à Rede 5G no Contexto da IoT

• Cleitianne Silva UFC
• Carina Oliveira IFCE
• Rossana Andrade UFC

DOI: https://doi.org/10.5753/sbrc.2025.5948

Resumo

A integração da IoT com as redes 5G amplia o número de dispositivos conectados e a complexidade do tráfego, intensificando desafios de segurança. Técnicas de Aprendizado de Máquina têm sido utilizadas para detectar padrões de ataques em redes. Este artigo apresenta o MARIA, uma solução desenvolvida para identificar e propor medidas de mitigação para ataques direcionados a dispositivos IoT em redes 5G. O Maria é composto por seis módulos e, como parte de seu processo, utiliza algoritmos supervisionados para detecção de ataques, viabilizando respostas rápidas. Uma avaliação do Maria é realizada em um testbed. Os resultados evidenciam a eficácia do MARIA na detecção em tempo real de diferentes tipos de ataques.

Palavras-chave: 5G, Segurança, Internet das coisas, Aprendizado de máquina

Referências

Aljuhani, A. (2021). Machine learning approaches for combating distributed denial of service attacks in modern networking environments. IEEE Access, 9, 42236–42264.

Berrar, D. (2019). Cross-validation. Encyclopedia of Bioinformatics and Computational Biology, 1(April), 542–545.

Dietterich, T. (1995). Overfitting and undercomputing in machine learning. ACM Computing Surveys (CSUR), 27(3), 326–327.

Farzaneh, B., Shahriar, N., Al Muktadir, A. H., Towhid, M. S., & Khosravani, M. S. (2024). DTL-5G: Deep transfer learning-based DDoS attack detection in 5G and beyond networks. Computer Communications, 228, 107927.

Garcia, S., Parmisano, A., and Erquiaga, M. J. (2021). Iot-23: a labeled dataset with malicious and benign iot network traffic (2020). URL: [link]. DOI: 10.5281/zenodo.4743746.

Khan, A. & Sharma, I. (2023). Tackling Okiru attacks in IoT with AI-driven detection and mitigation strategies. In International Conference on Power Energy, Environment & Intelligent Control (PEEIC) (pp. 336–341). IEEE.

Kim, Y.-E., Kim, Y.-S., & Kim, H. (2022). Effective feature selection methods to detect IoT DDoS attack in 5G core network. Sensors, 22(10), 3819.

Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Morgan Kaufman Publishing.

Kumar, S., Kumar, J. R. R., Mondal, D., Hemelatha, S., Diwakar, M. P., et al. (2023). A novel optimization strategy for detecting and preventing distributed denial of service attacks in wireless networks. In International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) (pp. 1–6). IEEE.

Kumari, P. & Jain, A. K. (2023). A comprehensive study of DDoS attacks over IoT network and their countermeasures. Computers & Security, 127, 103096.

Mohammadi, R., Lal, C., & Conti, M. (2023). HTTPScout: A machine learning-based countermeasure for HTTP flood attacks in SDN. International Journal of Information Security, 22(2), 367–379.

Nascita, A., Cerasuolo, F., Di Monda, D., Garcia, J. T. A., Montieri, A., & Pescape, A. (2022). Machine and deep learning approaches for IoT attack classification. In IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1–6). IEEE.

Nkosi, Z. G. & Mathonsi, T. E. (2024). A hybrid security algorithm for 5G-Internet of Things. In International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1–6). IEEE.

Ors, F. K., Aydın, M., Boğatarkan, A., & Levi, A. (2021). Scalable Wi-Fi intrusion detection for IoT systems. In 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1–6). IEEE.

Pakmehr, A., Aßmuth, A., Taheri, N., & Ghaffari, A. (2024). DDoS attack detection techniques in IoT networks: A survey. Cluster Computing, 27(10), 14637–14668.

Ramanauskaite, S. & Cenys, A. (2011). Taxonomy of DoS attacks and their countermeasures. Central European Journal of Computer Science, 1, 355–366.

Rao, G. S. & Subbarao, P. K. (2023). A novel framework for detection of DoS/DDoS attack using deep learning techniques, and an approach to mitigate the impact of DoS/DDoS attack in network environment. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 450–466.

Saheed, Y. K., Abiodun, A. I., Misra, S., Holone, M. K., & Colomo-Palacios, R. (2022). A machine learning-based intrusion detection for detecting Internet of Things network attacks. Alexandria Engineering Journal, 61(12), 9395–9409.

Samarakoon, S., Siriwardhana, Y., Porambage, P., Liyanage, M., Chang, S.-Y., Kim, J., Kim, J., & Ylianttila, M. (2022). 5G-NIDD: A comprehensive network intrusion detection dataset generated over 5G wireless network.

Yang, M. & Zhang, J. (2023). Data anomaly detection in the Internet of Things: A review of current trends and research challenges. International Journal of Advanced Computer Science and Applications, 14(9).