IoTSafe: Detecção de Ataques Baseada em Redes Neurais Profundas
Resumo
O uso massivo da Internet das Coisas (IoT), em ambientes urbanos, expõe sistemas de naturezas diversas a ataques. São recorrentes, na literatura, propostas de autenticação e criptografia ou sistemas de detecção de ataques, IDS (Intrusion Detection System). Entretanto, faltam soluções que consigam unir, eficientemente, essas duas táticas de segurança, principalmente se inseridas em arquiteturas de computação de borda. Este artigo propõe uma solução para detecção de ataques como complemento aos mecanismos de criptografia e autenticação da IoTSafe, apresentados em um trabalho anterior. O novo modulo, de detecção, é baseado em deep learning e implementa uma rede neural profunda para identificação de ataques. Especializado em comunicação via MQTT (Message Queuing Telemetry Transport), o módulo é implantado na camada fog da arquitetura. Para avaliar o módulo e a arquitetura IoTSafe completa, testes em três ambientes foram realizados, com indicativos de consumo de recursos em cada um. Um estudo de caso mostrou que a IoTSafe funciona com todos os mecanismos, módulos e camadas propostos. O desempenho do novo módulo evidencia a sua eficácia, tendo obtido uma acurácia de 99,57% e precisão de 99,66% na detecção de ataques.
Referências
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Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., and Ming, H. (2019). Ad-iot: Anomaly detection of iot cyberattacks in smart city using machine learning. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pages 0305-0310.
Ashiku, L. and Dagli, C. (2021). Network intrusion detection system using deep learning. Procedia Computer Science, 185:239-247.
Bonney, M. S., De Angelis, M., Dal Borgo, M., Andrade, L., Beregi, S., Jamia, N., and Wagg, D. J. (2022). Development of a digital twin operational platform using python flask. Data-Centric Engineering, 3.
Chockwanich, N. and Visoottiviseth, V. (2019). Intrusion detection by deep learning with tensorflow. In 2019 21st international conference on advanced communication technology (ICACT), pages 654-659. IEEE.
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Frustaci, M., Pace, P., Aloi, G., and Fortino, G. (2017). Evaluating critical security issues of the iot world: Present and future challenges. IEEE Internet of things journal, 5(4):2483-2495.
Garg, H. and Dave, M. (2019). Securing iot devices and securelyconnecting the dots using rest api and middleware. In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), pages 1-6. IEEE.
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., and Robles-Kelly, A. (2019). Deep learning-based intrusion detection for iot networks. In 2019 IEEE 24th pacific rim international symposium on dependable computing (PRDC), pages 256-25609. IEEE.
Islam, N., Farhin, F., Sultana, I., Kaiser, M. S., Rahman, M. S., Mahmud, M., Hosen, A., and Cho, G. H. (2021). Towards machine learning based intrusion detection in iot networks. Comput. Mater. Contin, 69(2):1801-1821.
Kirupakar, J. and Shalinie, S. M. (2019). Situation aware intrusion detection system design for industrial iot gateways. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), pages 1-6. IEEE.
Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100:779-796.
Lombardi, M., Pascale, F., and Santaniello, D. (2021). Internet of things: A general overview between architectures, protocols and applications. Information, 12(2):87.
McCormack, M., Vasudevan, A., Liu, G., Echeverría, S., O'Meara, K., Lewis, G., and Sekar, V. (2020). Towards an architecture for trusted edge iot security gateways. In 3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20). USENIX Association.
Prabavathy, S., Sundarakantham, K., and Shalinie, S. M. (2018). Design of cognitive fog computing for intrusion detection in internet of things. Journal of Communications and Networks, 20(3):291-298.
Sarhan, M., Layeghy, S., Moustafa, N., Gallagher, M., and Portmann, M. (2022). Feature extraction for machine learning-based intrusion detection in iot networks. Digital Communications and Networks.
Shafi, Q., Basit, A., Qaisar, S., Koay, A., and Welch, I. (2018). Fog-assisted sdn controlled framework for enduring anomaly detection in an iot network. IEEE Access, 6:73713-73723.
Shalaginov, A., Semeniuta, O., and Alazab, M. (2019). Meml: Resource-aware mqttbased machine learning for network attacks detection on iot edge devices. In Proceedings of the 12th IEEE/ACM Int. Conference on Utility and Cloud Computing Companion, pages 123-128.
Soukup, D., Hujňák, O., Štefunko, S., Krejčí, R., and Grešák, E. (2019). Security framework for iot and fog computing networks. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pages 87-92. IEEE.
Stejskal, B. J. (2018). Performance testing and analysis of qpid dispatch router.
Ullah, I. and Mahmoud, Q. H. (2020). A two-level flow-based anomalous activity detection system for iot networks. Electronics, 9(3):530.
Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., and Cambiaso, E. (2020). Mqttset, a new dataset for machine learning techniques on mqtt. Sensors, 20(22):6578.
Zarpelão, B. B., Miani, R. S., Kawakani, C. T., and de Alvarenga, S. C. (2017). A survey of intrusion detection in internet of things. Journal of Net. and Computer Applications, 84:25-37.
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.
Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., and Ming, H. (2019). Ad-iot: Anomaly detection of iot cyberattacks in smart city using machine learning. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pages 0305-0310.
Ashiku, L. and Dagli, C. (2021). Network intrusion detection system using deep learning. Procedia Computer Science, 185:239-247.
Bonney, M. S., De Angelis, M., Dal Borgo, M., Andrade, L., Beregi, S., Jamia, N., and Wagg, D. J. (2022). Development of a digital twin operational platform using python flask. Data-Centric Engineering, 3.
Chockwanich, N. and Visoottiviseth, V. (2019). Intrusion detection by deep learning with tensorflow. In 2019 21st international conference on advanced communication technology (ICACT), pages 654-659. IEEE.
dos Santos, F. C. and de Freitas Mini, R. A. (2021). Iotsafe-uma arquitetura baseada em fog computing para prover segurança em iot. In Anais do XIX Workshop de Computação em Clouds e Aplicações, pages 15-28. SBC.
Frustaci, M., Pace, P., Aloi, G., and Fortino, G. (2017). Evaluating critical security issues of the iot world: Present and future challenges. IEEE Internet of things journal, 5(4):2483-2495.
Garg, H. and Dave, M. (2019). Securing iot devices and securelyconnecting the dots using rest api and middleware. In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), pages 1-6. IEEE.
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., and Robles-Kelly, A. (2019). Deep learning-based intrusion detection for iot networks. In 2019 IEEE 24th pacific rim international symposium on dependable computing (PRDC), pages 256-25609. IEEE.
Islam, N., Farhin, F., Sultana, I., Kaiser, M. S., Rahman, M. S., Mahmud, M., Hosen, A., and Cho, G. H. (2021). Towards machine learning based intrusion detection in iot networks. Comput. Mater. Contin, 69(2):1801-1821.
Kirupakar, J. and Shalinie, S. M. (2019). Situation aware intrusion detection system design for industrial iot gateways. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), pages 1-6. IEEE.
Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100:779-796.
Lombardi, M., Pascale, F., and Santaniello, D. (2021). Internet of things: A general overview between architectures, protocols and applications. Information, 12(2):87.
McCormack, M., Vasudevan, A., Liu, G., Echeverría, S., O'Meara, K., Lewis, G., and Sekar, V. (2020). Towards an architecture for trusted edge iot security gateways. In 3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20). USENIX Association.
Prabavathy, S., Sundarakantham, K., and Shalinie, S. M. (2018). Design of cognitive fog computing for intrusion detection in internet of things. Journal of Communications and Networks, 20(3):291-298.
Sarhan, M., Layeghy, S., Moustafa, N., Gallagher, M., and Portmann, M. (2022). Feature extraction for machine learning-based intrusion detection in iot networks. Digital Communications and Networks.
Shafi, Q., Basit, A., Qaisar, S., Koay, A., and Welch, I. (2018). Fog-assisted sdn controlled framework for enduring anomaly detection in an iot network. IEEE Access, 6:73713-73723.
Shalaginov, A., Semeniuta, O., and Alazab, M. (2019). Meml: Resource-aware mqttbased machine learning for network attacks detection on iot edge devices. In Proceedings of the 12th IEEE/ACM Int. Conference on Utility and Cloud Computing Companion, pages 123-128.
Soukup, D., Hujňák, O., Štefunko, S., Krejčí, R., and Grešák, E. (2019). Security framework for iot and fog computing networks. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pages 87-92. IEEE.
Stejskal, B. J. (2018). Performance testing and analysis of qpid dispatch router.
Ullah, I. and Mahmoud, Q. H. (2020). A two-level flow-based anomalous activity detection system for iot networks. Electronics, 9(3):530.
Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., and Cambiaso, E. (2020). Mqttset, a new dataset for machine learning techniques on mqtt. Sensors, 20(22):6578.
Zarpelão, B. B., Miani, R. S., Kawakani, C. T., and de Alvarenga, S. C. (2017). A survey of intrusion detection in internet of things. Journal of Net. and Computer Applications, 84:25-37.
Publicado
22/05/2023
Como Citar
SANTOS, Fábio Coutinho dos; SANTOS, Aldri Luiz dos; DUARTE-FIGUEIREDO, Fátima.
IoTSafe: Detecção de Ataques Baseada em Redes Neurais Profundas. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 7. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 159-172.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2023.783.
