Detecção de Intrusão em Sistemas IoT Baseada em Comitê de Classificadores
Resumo
Aplicações IoT são, em geral, vulneráveis a ataques de usuários maliciosos devido à sua menor robustez atrelada à simplicidade de uso e onipresença dos dispositivos. Por outro lado, Sistemas de Detecção de Intrusão (IDS) têm sido utilizados com sucesso com a finalidade de analisar informações de um determinado sistema monitorado e detectar sinais de comportamento malicioso, o que torna possível alertar os administradores e adotar medidas corretivas de forma ágil. Este trabalho apresenta uma nova abordagem para detecção de intrusão denominada PaC (Preprocessing and Committee), a qual baseia-se na utilização de um comitê de classificadores. O PaC apresentou resultados superiores ao estado da arte, alcançando melhores valores de acurácia, precisão, recall e F1-score na detecção de ataques em aplicações IoT.
Referências
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., and Anwar, A. (2020). TON-IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access, 8:165130–165150.
Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., and Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electronics, 9(7):1177.
Balaji, S., Nathani, K., and Santhakumar, R. (2019). IoT technology, applications and challenges: a contemporary survey. Wireless personal communications, 108:363–388.
de Souza, C., Cardoso, J., and Westphall, C. (2021). Multiclass decomposition and artificial neural networks for intrusion detection and identification in internet of things environments. In Anais do XXI SBSeg, pages 85–98, Porto Alegre, RS, Brasil. SBC.
do Nascimento, E. J. F., Souza, A. H., and Mesquita, D. (2021). Improving graph variational autoencoders with multi-hop simple convolutions. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 105–110.
Gad, A. R., Nashat, A. A., and Barkat, T. M. (2021). Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access, 9:142206–142217.
Géron, A. (2019). Mãos à Obra: Aprendizado de Máquina com Scikit-Learn & TensorFlow. Alta Books.
Imad, M., Abul Hassan, M., Hussain Bangash, S., and Naimullah (2022). A comparative analysis of intrusion detection in IoT network using machine learning. In Big Data Analytics and Computational Intelligence for Cybersecurity, pages 149–163. Springer.
Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., and Xiong, N. N. (2021). PPSF: A privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities. IEEE Transactions on Network Science and Engineering, 8(3):2326–2341.
Mallet, J., Pryor, L., Dave, R., Seliya, N., Vanamala, M., and Sowells-Boone, E. (2022). Hold on and swipe: a touch-movement based continuous authentication schema based on machine learning. In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), pages 442–447. IEEE.
Mandal, K., Rajkumar, M., Ezhumalai, P., Jayakumar, D., and Yuvarani, R. (2020). Improved security using machine learning for IoT intrusion detection system. Materials Today: Proceedings.
Moustafa, N. (2019). New generations of internet of things datasets for cybersecurity applications based machine learning: TON-IoT datasets. In Proceedings of the eResearch Australasia Conference, Brisbane, Australia, pages 21–25.
Moustafa, N. (2021). A new distributed architecture for evaluating AI-based security systems at the edge: Network TON-IoT datasets. Sustainable Cities and Society, 72:102994.
Sarhan, M., Layeghy, S., Moustafa, N., and Portmann, M. (2021). Netflow datasets for machine learning-based network intrusion detection systems. pages 117–135. Springer.
Sharafaldin, I., Lashkari, A. H., and Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. 4th International Conference on Information Systems Security and Privacy (ICISSp), 1:108–116.
Tareq, I., Elbagoury, B. M., El-Regaily, S., and El-Horbaty, E.-S. M. (2022). Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT datasets using DL in cybersecurity for IoT. Applied Sciences, 12(19):9572.
Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications, pages 1–6. IEEE.
Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., and Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electronics, 9(7):1177.
Balaji, S., Nathani, K., and Santhakumar, R. (2019). IoT technology, applications and challenges: a contemporary survey. Wireless personal communications, 108:363–388.
de Souza, C., Cardoso, J., and Westphall, C. (2021). Multiclass decomposition and artificial neural networks for intrusion detection and identification in internet of things environments. In Anais do XXI SBSeg, pages 85–98, Porto Alegre, RS, Brasil. SBC.
do Nascimento, E. J. F., Souza, A. H., and Mesquita, D. (2021). Improving graph variational autoencoders with multi-hop simple convolutions. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 105–110.
Gad, A. R., Nashat, A. A., and Barkat, T. M. (2021). Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access, 9:142206–142217.
Géron, A. (2019). Mãos à Obra: Aprendizado de Máquina com Scikit-Learn & TensorFlow. Alta Books.
Imad, M., Abul Hassan, M., Hussain Bangash, S., and Naimullah (2022). A comparative analysis of intrusion detection in IoT network using machine learning. In Big Data Analytics and Computational Intelligence for Cybersecurity, pages 149–163. Springer.
Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., and Xiong, N. N. (2021). PPSF: A privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities. IEEE Transactions on Network Science and Engineering, 8(3):2326–2341.
Mallet, J., Pryor, L., Dave, R., Seliya, N., Vanamala, M., and Sowells-Boone, E. (2022). Hold on and swipe: a touch-movement based continuous authentication schema based on machine learning. In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), pages 442–447. IEEE.
Mandal, K., Rajkumar, M., Ezhumalai, P., Jayakumar, D., and Yuvarani, R. (2020). Improved security using machine learning for IoT intrusion detection system. Materials Today: Proceedings.
Moustafa, N. (2019). New generations of internet of things datasets for cybersecurity applications based machine learning: TON-IoT datasets. In Proceedings of the eResearch Australasia Conference, Brisbane, Australia, pages 21–25.
Moustafa, N. (2021). A new distributed architecture for evaluating AI-based security systems at the edge: Network TON-IoT datasets. Sustainable Cities and Society, 72:102994.
Sarhan, M., Layeghy, S., Moustafa, N., and Portmann, M. (2021). Netflow datasets for machine learning-based network intrusion detection systems. pages 117–135. Springer.
Sharafaldin, I., Lashkari, A. H., and Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. 4th International Conference on Information Systems Security and Privacy (ICISSp), 1:108–116.
Tareq, I., Elbagoury, B. M., El-Regaily, S., and El-Horbaty, E.-S. M. (2022). Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT datasets using DL in cybersecurity for IoT. Applied Sciences, 12(19):9572.
Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications, pages 1–6. IEEE.
Publicado
18/09/2023
Como Citar
RIBEIRO, Davyson S. et al.
Detecção de Intrusão em Sistemas IoT Baseada em Comitê de Classificadores. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 23. , 2023, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 431-444.
DOI: https://doi.org/10.5753/sbseg.2023.233648.
