Geração de Dados de Ataque em Internet das Coisas utilizando Redes Generativas Adversárias
Resumo
A análise de tráfego de dados gerados por dispositivos é fundamental para detecção e mitigação de ataques na Internet das Coisas. Contudo, dados públicos que representem ataques reais ainda são escassos. Visando aumentar a disponibilidade de dados, este trabalho apresenta um estudo do uso de Redes Generativas Adversárias (GANs) para gerar dados sintéticos de ataque em dispositivos IoT com alta fidelidade em relação aos dados reais, isto é, com características similares. Ao mesmo tempo visa garantir privacidade e que a utilidade dos dados sintéticos em tarefas de aprendizado de máquina sejam similares aos reais. Para isso, foram comparamos dois modelos de GANs, CTGAN e NetShare, utilizando como base um conjunto de dados contendo tráfego normal e com ataques em dispositivos IoT. Os resultados indicam que ambos os modelos de GANs são eficientes na geração de dados sintéticos, tanto em fidelidade quanto em qualidade. Entretanto, a CTGAN apresenta-se como o modelo mais eficiente, considerando tempo de execução e consumo de memória.Referências
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Cunha, V. C., Zavala, A. Z., Magoni, D., Inácio, P. R. M., and Freire, M. M. (2022). A complete review on the application of statistical methods for evaluating internet traffic usage. IEEE Access, 10:128433–128455.
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Esteban, C., Hyland, S. L., and Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633.
Gheisari, M., Alzubi, J., Zhang, X., Kose, U., and Saucedo, J. A. M. (2020). A new algorithm for optimization of quality of service in peer to peer wireless mesh networks. Wireless Networks, 26:4965–4973.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680.
Hossain, M. D., Ochiai, H., Doudou, F., and Kadobayashi, Y. (2020). Ssh and ftp brute-force attacks detection in computer networks: Lstm and machine learning approaches. In 2020 5th international conference on computer and communication systems (ICCCS), pages 491–497. IEEE.
Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation.
Kingma, D. P. and Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
Kumar, V. and Sinha, D. (2023). Synthetic attack data generation model applying generative adversarial network for intrusion detection. Computers & Security, 125:103054.
Marani, A. and Nehdi, M. L. (2022). Predicting shear strength of frp-reinforced con-crete beams using novel synthetic data driven deep learning. Engineering Structures, 257:114083.
Nekvi, R. I., Saha, S., Al Mtawa, Y., and Haque, A. (2023). Examining generative adversarial network for smart home ddos traffic generation. In 2023 International Symposium on Networks, Computers and Communications (ISNCC), pages 1–6. IEEE.
Pundir, M., Sandhu, J. K., and Kumar, A. (2021). Quality-of-service prediction techniques for wireless sensor networks. In Journal of Physics: Conference Series, volume 1950, page 012082. IOP Publishing.
Qian, C., Yu, W., Lu, C., Griffith, D., and Golmie, N. (2022). Toward generative adversarial networks for the industrial internet of things. IEEE Internet of Things Journal, 9(19):19147–19159.
Sebastian Garcia, Agustin Parmisano, . M. J. E. (2020). Iot-23: A labeled dataset with malicious and benign iot network traffic (version 1.0.0) [data set].
Shahid, M. R., Blanc, G., Jmila, H., Zhang, Z., and Debar, H. (2020). Generative deep learning for internet of things network traffic generation. In Pacific Rim International Symposium on Dependable Computing, pages 70–79. IEEE.
Sharafaldin, I., Gharib, A., Lashkari, A. H., and Ghorbani, A. A. (2018). Towards a reliable intrusion detection benchmark dataset. Software Networking, 2018(1):177–200.
Wang, Z., She, Q., and Ward, T. E. (2021). Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR), 54(2):1–38.
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. Advances in neural information processing systems, 32.
Yin, Y., Lin, Z., Jin, M., Fanti, G., and Sekar, V. (2022). Practical gan-based synthetic ip header trace generation using netshare. In Proceedings of the ACM SIGCOMM 2022 Conference, pages 458–472.
Alex, C., Creado, G., Almobaideen, W., Alghanam, O. A., and Saadeh, M. (2023). A comprehensive survey for iot security datasets taxonomy, classification and machine learning mechanisms. Computers & Security, page 103283.
Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein generative adversarial networks. In International conference on machine learning, pages 214–223. PMLR.
Borji, A. (2022). Pros and cons of gan evaluation measures: New developments. Computer Vision and Image Understanding, 215:103329.
Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale gan training for high fidelity natural image synthesis.
Brophy, E., Wang, Z., She, Q., and Ward, T. (2023). Generative adversarial networks in time series: A systematic literature review. ACM Computing Surveys, 55(10):1–31.
Cunha, V. C., Zavala, A. Z., Magoni, D., Inácio, P. R. M., and Freire, M. M. (2022). A complete review on the application of statistical methods for evaluating internet traffic usage. IEEE Access, 10:128433–128455.
Dash, A., Ye, J., and Wang, G. (2023). A review of generative adversarial networks (gans) and its applications in a wide variety of disciplines: From medical to remote sensing. IEEE Access.
Esteban, C., Hyland, S. L., and Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633.
Gheisari, M., Alzubi, J., Zhang, X., Kose, U., and Saucedo, J. A. M. (2020). A new algorithm for optimization of quality of service in peer to peer wireless mesh networks. Wireless Networks, 26:4965–4973.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680.
Hossain, M. D., Ochiai, H., Doudou, F., and Kadobayashi, Y. (2020). Ssh and ftp brute-force attacks detection in computer networks: Lstm and machine learning approaches. In 2020 5th international conference on computer and communication systems (ICCCS), pages 491–497. IEEE.
Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation.
Kingma, D. P. and Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
Kumar, V. and Sinha, D. (2023). Synthetic attack data generation model applying generative adversarial network for intrusion detection. Computers & Security, 125:103054.
Marani, A. and Nehdi, M. L. (2022). Predicting shear strength of frp-reinforced con-crete beams using novel synthetic data driven deep learning. Engineering Structures, 257:114083.
Nekvi, R. I., Saha, S., Al Mtawa, Y., and Haque, A. (2023). Examining generative adversarial network for smart home ddos traffic generation. In 2023 International Symposium on Networks, Computers and Communications (ISNCC), pages 1–6. IEEE.
Pundir, M., Sandhu, J. K., and Kumar, A. (2021). Quality-of-service prediction techniques for wireless sensor networks. In Journal of Physics: Conference Series, volume 1950, page 012082. IOP Publishing.
Qian, C., Yu, W., Lu, C., Griffith, D., and Golmie, N. (2022). Toward generative adversarial networks for the industrial internet of things. IEEE Internet of Things Journal, 9(19):19147–19159.
Sebastian Garcia, Agustin Parmisano, . M. J. E. (2020). Iot-23: A labeled dataset with malicious and benign iot network traffic (version 1.0.0) [data set].
Shahid, M. R., Blanc, G., Jmila, H., Zhang, Z., and Debar, H. (2020). Generative deep learning for internet of things network traffic generation. In Pacific Rim International Symposium on Dependable Computing, pages 70–79. IEEE.
Sharafaldin, I., Gharib, A., Lashkari, A. H., and Ghorbani, A. A. (2018). Towards a reliable intrusion detection benchmark dataset. Software Networking, 2018(1):177–200.
Wang, Z., She, Q., and Ward, T. E. (2021). Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR), 54(2):1–38.
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. Advances in neural information processing systems, 32.
Yin, Y., Lin, Z., Jin, M., Fanti, G., and Sekar, V. (2022). Practical gan-based synthetic ip header trace generation using netshare. In Proceedings of the ACM SIGCOMM 2022 Conference, pages 458–472.
Publicado
20/05/2024
Como Citar
RIBEIRO, Iran F.; BROTTO, Guilherme S. G.; COMARELA, Giovanni; MOTA, Vinícius F. S..
Geração de Dados de Ataque em Internet das Coisas utilizando Redes Generativas Adversárias. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 8. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 210-223.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2024.3377.
