Enhancing Cybersecurity of Automotive Ethernet Networks with Deep Learning-based Intrusion Detection Systems
Resumo
O aumento na quantidade de superfícies de ataque em veículos, impulsionado pela crescente presença de conectividade, em conjunto com a necessidade da Ethernet automotiva - uma tecnologia de rede intraveicular de alta largura de banda - evidencia a necessidade de mecanismos de segurança cibernética eficazes. Esta dissertação propõe dois sistemas de detecção de intrusão (IDSs - do inglês Intrusion Detection Systems) baseados em deep learning para a detecção de ciberataques em redes Ethernet automotivas. A primeira proposta apresenta um IDS baseado em uma rede neural convolucional otimizada por múltiplos critérios, projetada para otimizar, de forma simultânea, a precisão e o tempo de detecção, bem como os requisitos de armazenamento. A segunda proposta apresenta um IDS de múltiplos estágios, também baseado em deep learning, em que o primeiro estágio prioriza a rápida detecção de ataques, enquanto o segundo foca na obtenção de resultados mais precisos e também na classificação dos ataques. Como principais resultados desta pesquisa, destacam-se a publicação de um artigo no SBRC 2023 e outro no periódico Ad Hoc Networks.
Palavras-chave:
Sistemas de detecção de intrusão, Aprendizagem profunda, Ethernet automotiva
Referências
Alkhatib, N., Ghauch, H., and Danger, J.-L. (2021). SOME/IP intrusion detection using deep learning-based sequential models in automotive ethernet networks. In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pages 0954–0962.
Alkhatib, N., Mushtaq, M., Ghauch, H., and Danger, J.-L. (2022). Unsupervised network intrusion detection system for avtp in automotive ethernet networks. In 2022 IEEE Intelligent Vehicles Symposium (IV), page 1731–1738. IEEE Press.
Bianco, S., Cadene, R., Celona, L., and Napoletano, P. (2018). Benchmark analysis of representative deep neural network architectures. IEEE Access, 6:64270–64277.
Burke, K. (2019). How does a self-driving car see? [link]. Accessed: 2022-12-30.
Carmo, P., Freitas de Araujo-Filho, P., Campelo, D., Freitas, E., Filho, A. O., and Sadok, D. (2022). Machine learning-based intrusion detection system for automotive ethernet: Detecting cyber-attacks with a low-cost platform. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 196–209, Porto Alegre, RS, Brasil. SBC.
Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., Savage, S., Koscher, K., Czeskis, A., Roesner, F., and Kohno, T. (2011). Comprehensive experimental analyses of automotive attack surfaces. In 20th USENIX Security Symposium (USENIX Security 11), San Francisco, CA. USENIX Association.
da Luz, L., Freitas de Araujo-Filho, P., and Campelo, D. (2023). Multi-criteria optimized deep learning-based intrusion detection system for detecting cyberattacks in automotive ethernet networks. In Anais do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 197–210, Porto Alegre, RS, Brasil. SBC.
da Luz, L. F. M., Freitas de Araujo-Filho, P., and Campelo, D. R. (2024). Multi-stage deep learning-based intrusion detection system for automotive ethernet networks. Ad Hoc Networks, 162:103548.
Freitas de Araujo-Filho, P., Kaddoum, G., Campelo, D. R., Santos, A. G., Macêdo, D., and Zanchettin, C. (2020). Intrusion detection for cyber–physical systems using generative adversarial networks in fog environment. IEEE Internet of Things Journal, 8(8):6247–6256.
Ghosal, A. and Conti, M. (2020). Security issues and challenges in V2X : A Survey. Computer Networks, 169:107093.
Girish, S., Gupta, K., Singh, S., and Shrivastava, A. (2022). Lilnetx: Lightweight networks with extreme model compression and structured sparsification.
Han, M. L., Kwak, B. I., and Kim, H. K. (2023). TOW-IDS: Intrusion detection system based on three overlapped wavelets for automotive ethernet. IEEE Transactions on Information Forensics and Security, 18:411–422.
IEEE (2016). IEEE standard for a transport protocol for time-sensitive applications in bridged local area networks. IEEE Std 1722-2016 (Revision of IEEE Std 1722-2011), pages 1–233.
IEEE (2020). IEEE standard for local and metropolitan area networks–timing and synchronization for time-sensitive applications. IEEE Std 802.1AS-2020 (Revision of IEEE Std 802.1AS-2011), pages 1–421.
Jacobs, A. S., Beltiukov, R., Willinger, W., Ferreira, R. A., Gupta, A., and Granville, L. Z. (2022). Ai/ml for network security: The emperor has no clothes. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS ’22, page 1537–1551, New York, NY, USA. Association for Computing Machinery.
Jeong, S., Jeon, B., Chung, B., and Kim, H. K. (2021). Convolutional neural network-based intrusion detection system for AVTP streams in automotive ethernet-based networks. Vehicular Communications, 29:100338.
Jeong, S., Kim, H. K., Han, M. L., and Kwak, B. I. (2023). AERO: Automotive ethernet real-time observer for anomaly detection in in-vehicle networks. IEEE Transactions on Industrial Informatics, pages 1–12.
Jo, H. J. and Choi, W. (2021). A Survey of Attacks on Controller Area Networks and Corresponding Countermeasures. IEEE Transactions on Intelligent Transportation Systems, pages 1–19.
Liu, J., Zhang, S., Sun, W., and Shi, Y. (2017). In-vehicle network attacks and counter-measures: Challenges and future directions. IEEE Network, 31(5):50–58.
Matheus, K. and Königseder, T. (2021). Automotive Ethernet. Cambridge University Press.
Miller, C. and Valasek, C. (2015). Remote Exploitation of an Unaltered Passenger Vehicle. Defcon 23, 2015:1–91.
Shibly, K. H., Hossain, M. D., Inoue, H., Taenaka, Y., and Kadobayashi, Y. (2023). A feature-aware semi-supervised learning approach for automotive ethernet. In 2023 IEEE International Conference on Cyber Security and Resilience (CSR), pages 426–431.
UN Regulation 155 (2021). UN Regulation No. 155 - cyber security and cyber security management system. [link].
Wu, W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., and Li, K. (2020). A survey of intrusion detection for in-vehicle networks. IEEE Transactions on Intelligent Transportation Systems, 21(3):919–933.
Alkhatib, N., Mushtaq, M., Ghauch, H., and Danger, J.-L. (2022). Unsupervised network intrusion detection system for avtp in automotive ethernet networks. In 2022 IEEE Intelligent Vehicles Symposium (IV), page 1731–1738. IEEE Press.
Bianco, S., Cadene, R., Celona, L., and Napoletano, P. (2018). Benchmark analysis of representative deep neural network architectures. IEEE Access, 6:64270–64277.
Burke, K. (2019). How does a self-driving car see? [link]. Accessed: 2022-12-30.
Carmo, P., Freitas de Araujo-Filho, P., Campelo, D., Freitas, E., Filho, A. O., and Sadok, D. (2022). Machine learning-based intrusion detection system for automotive ethernet: Detecting cyber-attacks with a low-cost platform. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 196–209, Porto Alegre, RS, Brasil. SBC.
Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., Savage, S., Koscher, K., Czeskis, A., Roesner, F., and Kohno, T. (2011). Comprehensive experimental analyses of automotive attack surfaces. In 20th USENIX Security Symposium (USENIX Security 11), San Francisco, CA. USENIX Association.
da Luz, L., Freitas de Araujo-Filho, P., and Campelo, D. (2023). Multi-criteria optimized deep learning-based intrusion detection system for detecting cyberattacks in automotive ethernet networks. In Anais do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 197–210, Porto Alegre, RS, Brasil. SBC.
da Luz, L. F. M., Freitas de Araujo-Filho, P., and Campelo, D. R. (2024). Multi-stage deep learning-based intrusion detection system for automotive ethernet networks. Ad Hoc Networks, 162:103548.
Freitas de Araujo-Filho, P., Kaddoum, G., Campelo, D. R., Santos, A. G., Macêdo, D., and Zanchettin, C. (2020). Intrusion detection for cyber–physical systems using generative adversarial networks in fog environment. IEEE Internet of Things Journal, 8(8):6247–6256.
Ghosal, A. and Conti, M. (2020). Security issues and challenges in V2X : A Survey. Computer Networks, 169:107093.
Girish, S., Gupta, K., Singh, S., and Shrivastava, A. (2022). Lilnetx: Lightweight networks with extreme model compression and structured sparsification.
Han, M. L., Kwak, B. I., and Kim, H. K. (2023). TOW-IDS: Intrusion detection system based on three overlapped wavelets for automotive ethernet. IEEE Transactions on Information Forensics and Security, 18:411–422.
IEEE (2016). IEEE standard for a transport protocol for time-sensitive applications in bridged local area networks. IEEE Std 1722-2016 (Revision of IEEE Std 1722-2011), pages 1–233.
IEEE (2020). IEEE standard for local and metropolitan area networks–timing and synchronization for time-sensitive applications. IEEE Std 802.1AS-2020 (Revision of IEEE Std 802.1AS-2011), pages 1–421.
Jacobs, A. S., Beltiukov, R., Willinger, W., Ferreira, R. A., Gupta, A., and Granville, L. Z. (2022). Ai/ml for network security: The emperor has no clothes. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS ’22, page 1537–1551, New York, NY, USA. Association for Computing Machinery.
Jeong, S., Jeon, B., Chung, B., and Kim, H. K. (2021). Convolutional neural network-based intrusion detection system for AVTP streams in automotive ethernet-based networks. Vehicular Communications, 29:100338.
Jeong, S., Kim, H. K., Han, M. L., and Kwak, B. I. (2023). AERO: Automotive ethernet real-time observer for anomaly detection in in-vehicle networks. IEEE Transactions on Industrial Informatics, pages 1–12.
Jo, H. J. and Choi, W. (2021). A Survey of Attacks on Controller Area Networks and Corresponding Countermeasures. IEEE Transactions on Intelligent Transportation Systems, pages 1–19.
Liu, J., Zhang, S., Sun, W., and Shi, Y. (2017). In-vehicle network attacks and counter-measures: Challenges and future directions. IEEE Network, 31(5):50–58.
Matheus, K. and Königseder, T. (2021). Automotive Ethernet. Cambridge University Press.
Miller, C. and Valasek, C. (2015). Remote Exploitation of an Unaltered Passenger Vehicle. Defcon 23, 2015:1–91.
Shibly, K. H., Hossain, M. D., Inoue, H., Taenaka, Y., and Kadobayashi, Y. (2023). A feature-aware semi-supervised learning approach for automotive ethernet. In 2023 IEEE International Conference on Cyber Security and Resilience (CSR), pages 426–431.
UN Regulation 155 (2021). UN Regulation No. 155 - cyber security and cyber security management system. [link].
Wu, W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., and Li, K. (2020). A survey of intrusion detection for in-vehicle networks. IEEE Transactions on Intelligent Transportation Systems, 21(3):919–933.
Publicado
19/05/2025
Como Citar
MARQUES DA LUZ, Luigi F.; FREITAS DE ARAUJO-FILHO, Paulo; CAMPELO, Divanilson R..
Enhancing Cybersecurity of Automotive Ethernet Networks with Deep Learning-based Intrusion Detection Systems. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 222-231.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2025.6908.
