Migração Automatizada de VMs na Defesa de Brokers MQTT Contra Memory Denial of Service
Resumo
Um dos principais protocolos da Internet das Coisas (IoT) é o Message Queuing Telemetry Transport (MQTT). O protocolo MQTT permite a comunicação entre dispositivos IoT por meio de brokers, que atuam como pontos centrais na topologia da rede. Os brokers são alvos frequentes de ataques severos, como os de negação de serviço (DoS). Esses ataques podem levar à indisponibilidade de serviços importantes, comprometendo aplicações críticas, como monitoramento de saúde e agricultura de precisão. Um dos principais desafios nesse contexto está nas limitações dos mecanismos de defesa padrão. Esses mecanismos, como firewalls, geralmente aplicam configurações estáticas e, por isso, não conseguem responder a ataques dinâmicos. Este artigo aborda esse problema ao propor uma solução baseada na migração automatizada de máquinas virtuais como defesa para brokers. O algoritmo proposto permite múltiplas configurações e utiliza resultados de avaliação de desempenho como parâmetros para a tomada de decisão. Os resultados demonstram um efeito de mitigação superior a 75% nos melhores cenários. A técnica e o código estão disponíveis para a reprodução de pesquisas.
Palavras-chave:
Migração de VMs, MQTT, Memory Denial of Service, Bucket Algorithm, Internet of Things
Referências
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Fernandes, G., Rodrigues, J. J., Carvalho, L. F., Al-Muhtadi, J. F., and Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70, 447–489.
Gonçalves, C. F., Menasche, D. S., Avritzer, A., Antunes, N., and Vieira, M. (2023). Detecting anomalies through sequential performance analysis in virtualized environments. IEEE Access, 11, 70716–70740.
Islam, U., Al-Atawi, A., Alwageed, H. S., Ahsan, M., Awwad, F. A., and Abonazel, M. R. (2023). Real-time detection schemes for memory DoS (m-DOS) attacks on cloud computing applications. IEEE Access.
Krylovsk (2024). Krylovsk/mqtt-benchmark: Mqtt broker benchmarking tool, disponível em [link], acesso em dezembro/2024.
Kusumi, K. and Koide, H. (2024). MQTT-MTD: Integrating moving target defense into MQTT protocol as an alternative to TLS. In 2024 7th International Conference on Advanced Communication Technologies and Networking (CommNet), pages 1–8. IEEE.
Li, W., Manickam, S., Nanda, P., Al-Ani, A. K., Karuppayah, S., et al. (2024). Securing MQTT ecosystem: Exploring vulnerabilities, mitigations, and future trajectories. IEEE Access.
Light, R. A. (2017). Mosquitto: Server and client implementation of the MQTT protocol. Journal of Open Source Software, 2(13), 265.
Saputro, N., Tonyali, S., Aydeger, A., Akkaya, K., Rahman, M. A., and Uluagac, S. (2020). A review of moving target defense mechanisms for Internet of Things applications. Modeling and Design of Secure Internet of Things, pages 563–614.
Siddharthan, H., Deepa, T., and Chandhar, P. (2022). SENMQTT-SET: An intelligent intrusion detection in IoT-MQTT networks using ensemble multi-cascade features. IEEE Access, 10, 33095–33110.
Torquato, M., Jesus, B., Silva, F. A., and Cerqueira, E. (2024). Empirical observation of execution throttling as MQTT broker defense against memory denial of service attacks. In Proceedings of the 13th Latin-American Symposium on Dependable and Secure Computing, LADC ’24, pages 184–187, New York, NY, USA. Association for Computing Machinery.
Torquato, M., Maciel, P., and Vieira, M. (2021). PyMTDEvaluator: A tool for time-based moving target defense evaluation: Tool description paper. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), pages 357–366. IEEE.
Torquato, M. and Vieira, M. (2020). Moving target defense in cloud computing: A systematic mapping study. Computers & Security, 92, 101742.
Torquato, M. and Vieira, M. (2021). VM migration scheduling as moving target defense against memory DoS attacks: An empirical study. In 2021 IEEE Symposium on Computers and Communications (ISCC), pages 1–6. IEEE.
Zhang, T., Zhang, Y., and Lee, R. B. (2017). DoS attacks on your memory in cloud. In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pages 253–265.
Dohi, T., Trivedi, K. S., and Avritzer, A. (2020). Handbook of software aging and rejuvenation: fundamentals, methods, applications, and future directions. World Scientific.
Fernandes, G., Rodrigues, J. J., Carvalho, L. F., Al-Muhtadi, J. F., and Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70, 447–489.
Gonçalves, C. F., Menasche, D. S., Avritzer, A., Antunes, N., and Vieira, M. (2023). Detecting anomalies through sequential performance analysis in virtualized environments. IEEE Access, 11, 70716–70740.
Islam, U., Al-Atawi, A., Alwageed, H. S., Ahsan, M., Awwad, F. A., and Abonazel, M. R. (2023). Real-time detection schemes for memory DoS (m-DOS) attacks on cloud computing applications. IEEE Access.
Krylovsk (2024). Krylovsk/mqtt-benchmark: Mqtt broker benchmarking tool, disponível em [link], acesso em dezembro/2024.
Kusumi, K. and Koide, H. (2024). MQTT-MTD: Integrating moving target defense into MQTT protocol as an alternative to TLS. In 2024 7th International Conference on Advanced Communication Technologies and Networking (CommNet), pages 1–8. IEEE.
Li, W., Manickam, S., Nanda, P., Al-Ani, A. K., Karuppayah, S., et al. (2024). Securing MQTT ecosystem: Exploring vulnerabilities, mitigations, and future trajectories. IEEE Access.
Light, R. A. (2017). Mosquitto: Server and client implementation of the MQTT protocol. Journal of Open Source Software, 2(13), 265.
Saputro, N., Tonyali, S., Aydeger, A., Akkaya, K., Rahman, M. A., and Uluagac, S. (2020). A review of moving target defense mechanisms for Internet of Things applications. Modeling and Design of Secure Internet of Things, pages 563–614.
Siddharthan, H., Deepa, T., and Chandhar, P. (2022). SENMQTT-SET: An intelligent intrusion detection in IoT-MQTT networks using ensemble multi-cascade features. IEEE Access, 10, 33095–33110.
Torquato, M., Jesus, B., Silva, F. A., and Cerqueira, E. (2024). Empirical observation of execution throttling as MQTT broker defense against memory denial of service attacks. In Proceedings of the 13th Latin-American Symposium on Dependable and Secure Computing, LADC ’24, pages 184–187, New York, NY, USA. Association for Computing Machinery.
Torquato, M., Maciel, P., and Vieira, M. (2021). PyMTDEvaluator: A tool for time-based moving target defense evaluation: Tool description paper. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), pages 357–366. IEEE.
Torquato, M. and Vieira, M. (2020). Moving target defense in cloud computing: A systematic mapping study. Computers & Security, 92, 101742.
Torquato, M. and Vieira, M. (2021). VM migration scheduling as moving target defense against memory DoS attacks: An empirical study. In 2021 IEEE Symposium on Computers and Communications (ISCC), pages 1–6. IEEE.
Zhang, T., Zhang, Y., and Lee, R. B. (2017). DoS attacks on your memory in cloud. In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pages 253–265.
Publicado
19/05/2025
Como Citar
TORQUATO, Matheus; GONÇALVES, Charles F.; NOGUEIRA, Michele; ROSÁRIO, Denis; CERQUEIRA, Eduardo.
Migração Automatizada de VMs na Defesa de Brokers MQTT Contra Memory Denial of Service. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 168-181.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2025.5872.
