Prevenção e Detecção de Intrusões em Redes IoT: Um Mapeamento Sistemático de Soluções na Borda e na Nuvem
Resumo
Este artigo apresenta uma revisão sistemática sobre sistemas de detecção e prevenção de intrusão (IDS/IPS) em redes de Internet das Coisas (IoT), com foco na borda e na nuvem. A análise abrangeu 24 artigos selecionados de quatro bases de dados, avaliados quanto à qualidade e relevância. Os resultados indicam que soluções como detecção baseada em comportamento, assinaturas e anomalias têm sido exploradas para proteger redes IoT, com destaque para o aprendizado de máquina, especialmente o aprendizado federado. No entanto, desafios como a complexidade da rede e a diversidade de dispositivos ainda persistem. O estudo categoriza e compara abordagens, fornecendo parâmetros e métricas para replicação em futuras pesquisas sobre segurança em IoT. Além disso, contribui com diretrizes para experimentação e reprodutibilidade de resultados.Referências
Abba Ari, A. A., Ngangmo, O. K., Titouna, C., Thiare, O., Mohamadou, A., and Gueroui, A. M. (2024). Enabling privacy and security in cloud of things: Architecture, applications, security & privacy challenges. Applied Computing and Informatics.
Abou El Houda, Z., Brik, B., Ksentini, A., and Khoukhi, L. (2023a). A mec-based architecture to secure iot applications using federated deep learning. IEEE Internet of Things Magazine, 6(1):60–63.
Abou El Houda, Z., Moudoud, H., Brik, B., and Khoukhi, L. (2023b). Securing federated learning through blockchain and explainable ai for robust intrusion detection in iot networks. In IEEE Conference on Computer Communications Workshops (INFOCOM).
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., and Guizani, M. (2020). A survey of machine and deep learning methods for internet of things (iot) security. IEEE communications surveys & tutorials, 22(3):1646–1685.
Alotaibi, A. and Barnawi, A. (2023). Idsoft: A federated and softwarized intrusion detection framework for massive internet of things in 6g network. Journal of King Saud University-Computer and Information Sciences, 35(6):101575.
Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15):2787–2805.
Bukhari, S. M. S., Zafar, M. H., Abou Houran, M., Qadir, Z., Moosavi, S. K. R., and Sanfilippo, F. (2024). Enhancing cybersecurity in edge iiot networks: An asynchronous federated learning approach with a deep hybrid detection model. Internet of Things.
Chennoufi, S., Blanc, G., Jmila, H., and Kiennert, C. (2024). Sok: federated learning based network intrusion detection in 5g: context, state of the art and challenges. In 19th International Conference on Availability, Reliability and Security.
Fan, Y., Li, Y., Zhan, M., Cui, H., and Zhang, Y. (2020). Iotdefender: A federated transfer learning intrusion detection framework for 5g iot. In 2020 IEEE 14th international conference on big data science and engineering (BigDataSE), pages 88–95. IEEE.
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., and Shu, L. (2021). Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis. IEEe Access.
Friha, O., Ferrag, M. A., Benbouzid, M., Berghout, T., Kantarci, B., and Choo, K.-K. R. (2023). 2df-ids: Decentralized and differentially private federated learning-based intrusion detection system for industrial iot. Computers & Security, 127:103097.
Ge, M. et al. (2019). Deep learning-based intrusion detection for iot networks. In IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE.
Han, C., Li, T., Chen, Q., Wu, Y., and Qin, J. (2024). Distributed and collaborative lightweight edge federated learning for iot zombie devices detection. ACM Transactions on Sensor Networks.
Hernandez-Ramos, J. L., Karopoulos, G., Chatzoglou, E., Kouliaridis, V., Marmol, E., Gonzalez-Vidal, A., and Kambourakis, G. (2023). Intrusion detection based on federated learning: a systematic review. arXiv preprint arXiv:2308.09522.
Jimmy, F. (2024). Cyber security vulnerabilities and remediation through cloud security tools. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023.
Mahmoodi, A. B. Z. and Et Al. (2023). Autonomous federated learning for distributed intrusion detection systems in public networks. IEEE Access.
Meng, R., Shah, A. A., Jamshed, M. A., and Pezaros, D. (2024). Federated learning-based intrusion detection framework for internet of things and edge computing backed critical infrastructure. In IEEE International Conference on Communications Workshops.
Nguyen, D. C. and Et al. (2021). Federated learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials.
Nguyen, T.-A., He, J., Le, L. T., Bao, W., and Tran, N. H. (2023). Federated pca on grassmann manifold for anomaly detection in iot networks. In IEEE INFOCOM.
Noura, M., Atiquzzaman, M., and Gaedke, M. (2019). Interoperability in internet of things: Taxonomies and open challenges. Mobile networks and applications.
Rash, M., Orebaugh, A., and Clark, G. (2005). Intrusion prevention and active response: Deploying network and host IPS. Elsevier.
Roy, S., Li, J., and Bai, Y. (2023). Federated learning-based intrusion detection system for iot environments with locally adapted model. In IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud). IEEE.
Salim, M. M. and Et al. (2024). Fl-ctif: A federated learning based cti framework based on information fusion for secure iiot. Information Fusion.
Shen, J., Yang, W., Chu, Z., Fan, J., Niyato, D., and Lam, K.-Y. (2024). Effective intrusion detection in heterogeneous internet-of-things networks via ensemble knowledge distillation-based federated learning. In IEEE Inter. Conference on Communications.
Shirazi, S. N. (2017). Anomaly Detection for Resilience in Cloud Computing Infrastructures. Lancaster University (United Kingdom).
Singh, P. and Et Al. (2022). Dew-cloud-based hierarchical federated learning for intrusion detection in iomt. IEEE journal of biomedical and health informatics.
Xiang, H., Zhang, X., Xu, X., Beheshti, A., Qi, L., Hong, Y., and Dou, W. (2024). Federated learning-based anomaly detection with isolation forest in the iot-edge continuum. ACM Transactions on Multimedia Computing, Communications and Applications.
Yang, Z., Chen, M., Wong, K.-K., Poor, H. V., and Cui, S. (2022). Federated learning for 6g: Applications, challenges, and opportunities. Engineering, 8:33–41.
Zhang, X., Wang, Y., Cai, Y., He, Y., Chen, X., and Jin, S. (2022). Intrusion detection based on data privacy in cloud-edge collaborative computing using federated learning. In Inter. Conference on Network and Information Systems for Computers (ICNISC).
Abou El Houda, Z., Brik, B., Ksentini, A., and Khoukhi, L. (2023a). A mec-based architecture to secure iot applications using federated deep learning. IEEE Internet of Things Magazine, 6(1):60–63.
Abou El Houda, Z., Moudoud, H., Brik, B., and Khoukhi, L. (2023b). Securing federated learning through blockchain and explainable ai for robust intrusion detection in iot networks. In IEEE Conference on Computer Communications Workshops (INFOCOM).
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., and Guizani, M. (2020). A survey of machine and deep learning methods for internet of things (iot) security. IEEE communications surveys & tutorials, 22(3):1646–1685.
Alotaibi, A. and Barnawi, A. (2023). Idsoft: A federated and softwarized intrusion detection framework for massive internet of things in 6g network. Journal of King Saud University-Computer and Information Sciences, 35(6):101575.
Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15):2787–2805.
Bukhari, S. M. S., Zafar, M. H., Abou Houran, M., Qadir, Z., Moosavi, S. K. R., and Sanfilippo, F. (2024). Enhancing cybersecurity in edge iiot networks: An asynchronous federated learning approach with a deep hybrid detection model. Internet of Things.
Chennoufi, S., Blanc, G., Jmila, H., and Kiennert, C. (2024). Sok: federated learning based network intrusion detection in 5g: context, state of the art and challenges. In 19th International Conference on Availability, Reliability and Security.
Fan, Y., Li, Y., Zhan, M., Cui, H., and Zhang, Y. (2020). Iotdefender: A federated transfer learning intrusion detection framework for 5g iot. In 2020 IEEE 14th international conference on big data science and engineering (BigDataSE), pages 88–95. IEEE.
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., and Shu, L. (2021). Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis. IEEe Access.
Friha, O., Ferrag, M. A., Benbouzid, M., Berghout, T., Kantarci, B., and Choo, K.-K. R. (2023). 2df-ids: Decentralized and differentially private federated learning-based intrusion detection system for industrial iot. Computers & Security, 127:103097.
Ge, M. et al. (2019). Deep learning-based intrusion detection for iot networks. In IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE.
Han, C., Li, T., Chen, Q., Wu, Y., and Qin, J. (2024). Distributed and collaborative lightweight edge federated learning for iot zombie devices detection. ACM Transactions on Sensor Networks.
Hernandez-Ramos, J. L., Karopoulos, G., Chatzoglou, E., Kouliaridis, V., Marmol, E., Gonzalez-Vidal, A., and Kambourakis, G. (2023). Intrusion detection based on federated learning: a systematic review. arXiv preprint arXiv:2308.09522.
Jimmy, F. (2024). Cyber security vulnerabilities and remediation through cloud security tools. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023.
Mahmoodi, A. B. Z. and Et Al. (2023). Autonomous federated learning for distributed intrusion detection systems in public networks. IEEE Access.
Meng, R., Shah, A. A., Jamshed, M. A., and Pezaros, D. (2024). Federated learning-based intrusion detection framework for internet of things and edge computing backed critical infrastructure. In IEEE International Conference on Communications Workshops.
Nguyen, D. C. and Et al. (2021). Federated learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials.
Nguyen, T.-A., He, J., Le, L. T., Bao, W., and Tran, N. H. (2023). Federated pca on grassmann manifold for anomaly detection in iot networks. In IEEE INFOCOM.
Noura, M., Atiquzzaman, M., and Gaedke, M. (2019). Interoperability in internet of things: Taxonomies and open challenges. Mobile networks and applications.
Rash, M., Orebaugh, A., and Clark, G. (2005). Intrusion prevention and active response: Deploying network and host IPS. Elsevier.
Roy, S., Li, J., and Bai, Y. (2023). Federated learning-based intrusion detection system for iot environments with locally adapted model. In IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud). IEEE.
Salim, M. M. and Et al. (2024). Fl-ctif: A federated learning based cti framework based on information fusion for secure iiot. Information Fusion.
Shen, J., Yang, W., Chu, Z., Fan, J., Niyato, D., and Lam, K.-Y. (2024). Effective intrusion detection in heterogeneous internet-of-things networks via ensemble knowledge distillation-based federated learning. In IEEE Inter. Conference on Communications.
Shirazi, S. N. (2017). Anomaly Detection for Resilience in Cloud Computing Infrastructures. Lancaster University (United Kingdom).
Singh, P. and Et Al. (2022). Dew-cloud-based hierarchical federated learning for intrusion detection in iomt. IEEE journal of biomedical and health informatics.
Xiang, H., Zhang, X., Xu, X., Beheshti, A., Qi, L., Hong, Y., and Dou, W. (2024). Federated learning-based anomaly detection with isolation forest in the iot-edge continuum. ACM Transactions on Multimedia Computing, Communications and Applications.
Yang, Z., Chen, M., Wong, K.-K., Poor, H. V., and Cui, S. (2022). Federated learning for 6g: Applications, challenges, and opportunities. Engineering, 8:33–41.
Zhang, X., Wang, Y., Cai, Y., He, Y., Chen, X., and Jin, S. (2022). Intrusion detection based on data privacy in cloud-edge collaborative computing using federated learning. In Inter. Conference on Network and Information Systems for Computers (ICNISC).
Publicado
20/07/2025
Como Citar
ALVES, Daniel Walmir dos Santos; DALMAZO, Bruno; RIKER, Andre; ROCHA FILHO, Geraldo P.; IMMICH, Roger.
Prevenção e Detecção de Intrusões em Redes IoT: Um Mapeamento Sistemático de Soluções na Borda e na Nuvem. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 17. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 111-120.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2025.9168.
