Um Framework Gerador de Tráfego para Detecção de Intrusões em Redes CAN
Resumo
As redes Controller Area Network (CAN) permitem comunicação intraveicular entre as Unidades Eletrònicas de Controle (ECU) e comunicação externa via WiFi, Bluetooth e USB, tornando-as vulneráveis a ataques cibernéticos. Este trabalho apresenta um framework gerador de conjuntos de dados para ajudar na detecção de intrusões em redes CAN, utilizando GANs (Generative Adversarial Networks) e VAEs (Variational Autoencoders). GANs criam datasets com distribuição similar aos dados reais, enquanto VAEs capturam a variabilidade, resultando em conjuntos de dados realistas e variados. Resultados preliminares mostram que o método proposto gera datasets de qualidade e variabilidade adequadas, podendo ser adaptado para outros ambientes.Referências
Aliwa, E., Rana, O., Perera, C., and Burnap, P. (2022). Cyberattacks and countermeasures for in-vehicle networks. ACM Computing Surveys, 54(1):1–37.
Asaoka, R., Murata, H., Matsuura, M., Fujino, Y., Yanagisawa, M., and Yamashita, T. (2020). Improving the structure–function relationship in glaucomatous visual fields by using a deep learning–based noise reduction approach. Ophthalmology Glaucoma, 3(3):210–217.
Avatefipour, O. and Malik, H. (2017). State-of-the-art survey on in-vehicle network communication “can-bus” security and vulnerabilities. International Journal of Computer Science and Network, pages 720–727.
Chougule, A., Agrawal, K., and Chamola, V. (2023). Scan-gan: Generative adversarial network based synthetic data generation technique for controller area network. IEEE Internet of Things Magazine, 6(3):126–130.
Dresch, F. N., Scherer, F. H., Quincozes, S. E., and Kreutz, D. L. (2024). Modelos interpretáveis com inteligência artificial explicável (XAI) na detecção de intrusões em redes intra-veiculares controller area network (CAN). In Anais do XIX Simpósio Brasileiro de Segurança da Informaçao e de Sistemas Computacionais. SBC.
Graving, J. M. and Couzin, I. D. (2020). Vae-sne: A deep generative model for simultaneous dimensionality reduction and clustering. BioRxiv.
Han, M. L., Kwak, B. I., and Kim, H. K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular Communications, 14:52–63.
Khan, M. H., Javed, A. R., Iqbal, Z., Asim, M., and Awad, A. I. (2024). DivaCAN: Detecting in-vehicle intrusion attacks on a controller area network using ensemble learning. Computers & Security, 139:103712.
Lee, H., Jeong, S. H., and Kim, H. K. (2017). Otids: A novel intrusion detection system for in-vehicle network by using remote frame. In 2017 15th Annual Conference on Privacy, Security and Trust (PST), volume 00, pages 57–5709.
Lin, J. (1991). Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory, 37(1):145–151.
Mahmud, M. S., Huang, J. Z., and Fu, X. (2020). Variational autoencoder-based dimensionality reduction for high-dimensional small-sample data classification. International Journal of Computational Intelligence and Applications, 19(1).
Miguel, M. M., Armignol, J. M., and Garcia, F. (2022). Vehicles trajectory prediction using recurrent vae network. IEEE Access, 10:32742–32749.
Pan, Z., Wang, J., Liao, W., Chen, H., Yuan, D., Zhu, W., Fang, X., and Zhu, Z. (2019). Data-driven ev load profiles generation using a variational autoencoder. Energies, 12(5):849.
Pollicino, F., Stabili, D., and Marchetti, M. (2024). Performance comparison of timing-based anomaly detectors for controller area network: A reproducible study. ACM Transactions on Cyber-Physical Systems, 8(2):1–24.
Razghandi, M., Zhou, H., Erol-Kantarci, M., and Turgut, D. (2024). Smart home energy management: Vae-gan synthetic dataset generator and q-learning. IEEE Transactions on Smart Grid, 15(2):1562–1573.
Scherer, F. H., Dresch, F. N., Quincozes, S. E., Kreutz, D., and Quincozes, V. E. (2024a). IWSHAP: Um método de seleção incremental de características para redes CAN baseado em Inteligência Artificial Explicável (XAI). In Anais do XXIV Simpósio Brasileiro de Segurança da Informaçao e de Sistemas Computacionais. SBC.
Scherer, F. H., Dresch, F. N., Quincozes, S. E., Kreutz, D., and Quincozes, V. E. (2024b). IWSHAP: Uma ferramenta para seleção incremental de características utilizando IWSS e SHAP. In Anais Estendidos do XXIV Simpósio Brasileiro de Segurança da Informaçao e de Sistemas Computacionais. SBC.
Seo, E., Song, H. M., and Kim, H. K. (2018). Gids: Gan based intrusion detection system for in-vehicle network. In 2018 16th Annual Conference on Privacy, Security and Trust (PST), pages 1–6.
Smirti, D., Medha, P., and Weiqing, S. (2020). A comparative study on contemporary intrusion detection datasets for machine learning research. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI).
Wang, Q., Qian, Y., Lu, Z., Shoukry, Y., and Qu, G. (2018). A delay based plug-in-monitor for intrusion detection in controller area network. In 2018 Asian Hardware Oriented Security and Trust Symposium (AsianHOST), pages 86–91, Hong Kong. IEEE.
Asaoka, R., Murata, H., Matsuura, M., Fujino, Y., Yanagisawa, M., and Yamashita, T. (2020). Improving the structure–function relationship in glaucomatous visual fields by using a deep learning–based noise reduction approach. Ophthalmology Glaucoma, 3(3):210–217.
Avatefipour, O. and Malik, H. (2017). State-of-the-art survey on in-vehicle network communication “can-bus” security and vulnerabilities. International Journal of Computer Science and Network, pages 720–727.
Chougule, A., Agrawal, K., and Chamola, V. (2023). Scan-gan: Generative adversarial network based synthetic data generation technique for controller area network. IEEE Internet of Things Magazine, 6(3):126–130.
Dresch, F. N., Scherer, F. H., Quincozes, S. E., and Kreutz, D. L. (2024). Modelos interpretáveis com inteligência artificial explicável (XAI) na detecção de intrusões em redes intra-veiculares controller area network (CAN). In Anais do XIX Simpósio Brasileiro de Segurança da Informaçao e de Sistemas Computacionais. SBC.
Graving, J. M. and Couzin, I. D. (2020). Vae-sne: A deep generative model for simultaneous dimensionality reduction and clustering. BioRxiv.
Han, M. L., Kwak, B. I., and Kim, H. K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular Communications, 14:52–63.
Khan, M. H., Javed, A. R., Iqbal, Z., Asim, M., and Awad, A. I. (2024). DivaCAN: Detecting in-vehicle intrusion attacks on a controller area network using ensemble learning. Computers & Security, 139:103712.
Lee, H., Jeong, S. H., and Kim, H. K. (2017). Otids: A novel intrusion detection system for in-vehicle network by using remote frame. In 2017 15th Annual Conference on Privacy, Security and Trust (PST), volume 00, pages 57–5709.
Lin, J. (1991). Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory, 37(1):145–151.
Mahmud, M. S., Huang, J. Z., and Fu, X. (2020). Variational autoencoder-based dimensionality reduction for high-dimensional small-sample data classification. International Journal of Computational Intelligence and Applications, 19(1).
Miguel, M. M., Armignol, J. M., and Garcia, F. (2022). Vehicles trajectory prediction using recurrent vae network. IEEE Access, 10:32742–32749.
Pan, Z., Wang, J., Liao, W., Chen, H., Yuan, D., Zhu, W., Fang, X., and Zhu, Z. (2019). Data-driven ev load profiles generation using a variational autoencoder. Energies, 12(5):849.
Pollicino, F., Stabili, D., and Marchetti, M. (2024). Performance comparison of timing-based anomaly detectors for controller area network: A reproducible study. ACM Transactions on Cyber-Physical Systems, 8(2):1–24.
Razghandi, M., Zhou, H., Erol-Kantarci, M., and Turgut, D. (2024). Smart home energy management: Vae-gan synthetic dataset generator and q-learning. IEEE Transactions on Smart Grid, 15(2):1562–1573.
Scherer, F. H., Dresch, F. N., Quincozes, S. E., Kreutz, D., and Quincozes, V. E. (2024a). IWSHAP: Um método de seleção incremental de características para redes CAN baseado em Inteligência Artificial Explicável (XAI). In Anais do XXIV Simpósio Brasileiro de Segurança da Informaçao e de Sistemas Computacionais. SBC.
Scherer, F. H., Dresch, F. N., Quincozes, S. E., Kreutz, D., and Quincozes, V. E. (2024b). IWSHAP: Uma ferramenta para seleção incremental de características utilizando IWSS e SHAP. In Anais Estendidos do XXIV Simpósio Brasileiro de Segurança da Informaçao e de Sistemas Computacionais. SBC.
Seo, E., Song, H. M., and Kim, H. K. (2018). Gids: Gan based intrusion detection system for in-vehicle network. In 2018 16th Annual Conference on Privacy, Security and Trust (PST), pages 1–6.
Smirti, D., Medha, P., and Weiqing, S. (2020). A comparative study on contemporary intrusion detection datasets for machine learning research. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI).
Wang, Q., Qian, Y., Lu, Z., Shoukry, Y., and Qu, G. (2018). A delay based plug-in-monitor for intrusion detection in controller area network. In 2018 Asian Hardware Oriented Security and Trust Symposium (AsianHOST), pages 86–91, Hong Kong. IEEE.
Publicado
16/09/2024
Como Citar
F. JUNIOR, Luiz; VARGAS, Paulo Sérgio M.; LIMA, Paulo Vitor C.; QUINCOZES, Silvio E..
Um Framework Gerador de Tráfego para Detecção de Intrusões em Redes CAN. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 24. , 2024, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 871-877.
DOI: https://doi.org/10.5753/sbseg.2024.241619.
