IoTSHIELD: Sistema de Detecção de Intrusão contra Botnets para Roteadores e Dispositivos de Borda
Resumo
A proliferação de dispositivos IoT vulneráveis tem impulsionado botnets destrutivas. Este artigo propõe um Sistema de Detecção de Intrusão (IDS) eficiente contra botnets projetado para roteadores e dispositivos de borda. A solução monitora o tráfego local e emprega o algoritmo Random Forest para identificar padrões anômalos e isolar equipamentos infectados pelo malware Mirai. A metodologia foi validada com mais de 318 mil fluxos de rede, alcançando 99,94% de acurácia e F1-Score. Em testes simulando hardware restrito (monocore de 1,15 GHz e 128 MB de RAM), o IDS obteve throughput superior a 578 mil pacotes por segundo, consumindo apenas 36,8 MB de RAM, indicando viabilidade e alto desempenho na borda.Referências
Antonakakis, M., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J., Durumeric, Z., Halderman, J. A., Invernizzi, L., Kallitsis, M., Kumar, D., Lever, C., Ma, Z., Mason, J., Menscher, D., Seaman, C., Sullivan, N., Thomas, K., and Zhou, Y. (2017). Understanding the mirai botnet. In 26th USENIX Security Symposium (USENIX Security 17), pages 1093–1110, Vancouver, BC. USENIX Association.
Awajan, A. (2023). A novel deep learning-based intrusion detection system for iot networks. Computers, 12(2).
Barkah, A. S., Selamat, S. R., Abidin, Z. Z., and Wahyudi, R. (2023). Impact of data balancing and feature selection on machine learning-based network intrusion detection. International Journal on Informatics Visualization.
Batista, G. E. A. P. A., Prati, R. C., and Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl., 6(1):20–29.
Bhavsar, Y. and Jhaveri, R. H. (2023). Anomaly-based intrusion detection system for iot application. Discover Internet of Things.
Bouke, M. A., Zaid, S. A., Abdullah, A., et al. (2024). Implications of data leakage in machine learning preprocessing: A multi-domain investigation. Preprint (Version 1) available at Research Square.
Canadian Institute for Cybersecurity (2017). Intrusion detection evaluation dataset (cic-ids2017).
Cloudflare (2025). Ddos threat report 2025 q3.
Elrawy, M. F., Awad, A. I., and Hamed, H. F. A. (2018). Intrusion detection systems for iot-based smart environments: a survey. Journal of Cloud Computing.
Gamblin, J. Mirai-source-code: Repositório de código fonte do mirai.
Guimarães, M. R. (2026). Iotshield: Sistema de detecção de intrusão contra botnets.
Ioannou, C., Charalambus, A., and Vassiliou, V. (2021). Decentralized dedicated intrusion detection security agents for iot networks. In 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), pages 414–419.
Kononenko, I. and Hong, S. J. (1997). Attribute selection for modelling. Future Generation Computer Systems, 13(2):181–195. Data Mining.
Kumar, V., Sinha, D., Das, A. K., Pandey, S. C., and Goswami, R. T. (2019). Uids: a unified intrusion detection system for iot environment. Evolutionary Intelligence.
Lee, J. Implementação mirai.
Liu, M., Yi, B., Huang, L., Yan, X., Wang, J., Yan, R., and Wang, X. (2025). Enhancing network simulation with container: A scalable and real-time supported platform. In 2025 IEEE 25th International Conference on Communication Technology (ICCT), pages 379–383.
Livadas, C., Walsh, R., Lapsley, D., and Strayer, W. T. (2006). Usilng machine learning technliques to identify botnet traffic. In Proceedings. 2006 31st IEEE Conference on Local Computer Networks, pages 967–974.
Rahman, M. M., Shakil, S. A., and Mustakim, M. R. (2025). A survey on intrusion detection system in iot networks. Cyber Security and Applications, 3:100082.
Silva, S. S., Silva, R. M., Pinto, R. C., and Salles, R. M. (2013). Botnets: A survey. Computer Networks, 57(2):378–403. Botnet Activity: Analysis, Detection and Shutdown.
Stratosphere Laboratory. A labeled dataset with malicious and benign iot network traffic (iot-23).
Yuan, X., Liu, S., Feng, W., and Dauphin, G. (2023). Feature importance ranking of random forest-based end-to-end learning algorithm. Remote Sensing, 15(21).
Awajan, A. (2023). A novel deep learning-based intrusion detection system for iot networks. Computers, 12(2).
Barkah, A. S., Selamat, S. R., Abidin, Z. Z., and Wahyudi, R. (2023). Impact of data balancing and feature selection on machine learning-based network intrusion detection. International Journal on Informatics Visualization.
Batista, G. E. A. P. A., Prati, R. C., and Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl., 6(1):20–29.
Bhavsar, Y. and Jhaveri, R. H. (2023). Anomaly-based intrusion detection system for iot application. Discover Internet of Things.
Bouke, M. A., Zaid, S. A., Abdullah, A., et al. (2024). Implications of data leakage in machine learning preprocessing: A multi-domain investigation. Preprint (Version 1) available at Research Square.
Canadian Institute for Cybersecurity (2017). Intrusion detection evaluation dataset (cic-ids2017).
Cloudflare (2025). Ddos threat report 2025 q3.
Elrawy, M. F., Awad, A. I., and Hamed, H. F. A. (2018). Intrusion detection systems for iot-based smart environments: a survey. Journal of Cloud Computing.
Gamblin, J. Mirai-source-code: Repositório de código fonte do mirai.
Guimarães, M. R. (2026). Iotshield: Sistema de detecção de intrusão contra botnets.
Ioannou, C., Charalambus, A., and Vassiliou, V. (2021). Decentralized dedicated intrusion detection security agents for iot networks. In 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), pages 414–419.
Kononenko, I. and Hong, S. J. (1997). Attribute selection for modelling. Future Generation Computer Systems, 13(2):181–195. Data Mining.
Kumar, V., Sinha, D., Das, A. K., Pandey, S. C., and Goswami, R. T. (2019). Uids: a unified intrusion detection system for iot environment. Evolutionary Intelligence.
Lee, J. Implementação mirai.
Liu, M., Yi, B., Huang, L., Yan, X., Wang, J., Yan, R., and Wang, X. (2025). Enhancing network simulation with container: A scalable and real-time supported platform. In 2025 IEEE 25th International Conference on Communication Technology (ICCT), pages 379–383.
Livadas, C., Walsh, R., Lapsley, D., and Strayer, W. T. (2006). Usilng machine learning technliques to identify botnet traffic. In Proceedings. 2006 31st IEEE Conference on Local Computer Networks, pages 967–974.
Rahman, M. M., Shakil, S. A., and Mustakim, M. R. (2025). A survey on intrusion detection system in iot networks. Cyber Security and Applications, 3:100082.
Silva, S. S., Silva, R. M., Pinto, R. C., and Salles, R. M. (2013). Botnets: A survey. Computer Networks, 57(2):378–403. Botnet Activity: Analysis, Detection and Shutdown.
Stratosphere Laboratory. A labeled dataset with malicious and benign iot network traffic (iot-23).
Yuan, X., Liu, S., Feng, W., and Dauphin, G. (2023). Feature importance ranking of random forest-based end-to-end learning algorithm. Remote Sensing, 15(21).
Publicado
01/09/2026
Como Citar
GUIMARÃES, Magno Ramos; MASCARENHAS, Dalbert Matos.
IoTSHIELD: Sistema de Detecção de Intrusão contra Botnets para Roteadores e Dispositivos de Borda. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA E DE GRADUAÇÃO - SIMPÓSIO BRASILEIRO DE CIBERSEGURANÇA (SBSEG), 26. , 2026, Armação dos Búzios/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 566-577.
DOI: https://doi.org/10.5753/sbseg_estendido.2026.29428.
