Recuperação do Serviço de Pelotão de Veículos Autônomos e Conectados Baseado em Modelos Virtuais Contra Ataques na Rede e Sensores
Resumo
A formação em pelotão de veículos autônomos e conectados (VACs) é a estrutura topológica mais comum de VACs, onde veículos trafegam relativamente próximos, respondendo aos comandos do líder e provendo serviços de transporte autônomo e inteligente. Contudo, com a evolução dos pelotões de VACs vários tipos de ataques de injeção de dados falsos em redes e sensores têm comprometido a estabilidade do pelotão, resultando na fragmentação ou, no pior caso, em serias colisões. Este trabalho propõe um mecanismo, denominada PReCAV, para a recuperação cooperativa de pelotão de VACs diante de ataques de injeção de dados falsos, apoiada por modelos virtuais e físicos do sistema, a fim de manter resiliência em meio à ataques em rede e sensores. Uma avaliação por simulação mostrou que o PReCAV manteve a estabilidade do pelotão ao prover resiliência, privando-o de variações de 4,42m/s2 de aceleração, 13m/s de velocidade e 40m de distância entre veículos em ataques na rede e variações de 4,43m/s2, 10,7m/s e 33,4m em ataques nos sensores.
Referências
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Mushtaq, A., Haq, I. u., Nabi, W. u., Khan, A., and Shafiq, O. (2021). Traffic flow management of autonomous vehicles using platooning and collision avoidance strategies. Electronics, 10(10).
Onishi, H. (2018). A survey: Engineering challenges to implement vanet security. In 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pages 1–6.
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Shoukry, Y. et al. (2013). Non-invasive spoofing attacks for anti-lock braking systems. In Cryptographic Hardware and Embedded Systems CHES 2013, pages 55–72, Berlin, Heidelberg.
Sun, G. et al. (2021). Strategic mitigation against wireless attacks on autonomous platoons. In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, pages 69–84, Cham. Springer International Publishing.
Tippenhauer, N. O. et al. (2011). On the requirements for successful gps spoofing attacks. In Proceedings of the 18th ACM CCCS, CCS ’11, page 75–86, New York, NY, USA.
Trippel, T. et al. (2017). Walnut: Waging doubt on the integrity of mems accelerometers with acoustic injection attacks. In 2017 IEEE EuroS P, pages 3–18.
Yang, T. and Lv, C. (2021). A secure sensor fusion framework for connected and automated vehicles under sensor attacks. IEEE Internet of Things Journal, pages 1–1.
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Back, J., Choi, S., and Shin, Y. (2019). A study on the election of suitable leader vehicle in vehicle platooning using the raft algorithm. In 2019 Eleventh ICUFN, pages 756–761.
Bang, S. and Ahn, S. (2017). Platooning strategy for connected and autonomous vehicles: Transition from light traffic. Transportation Research Record, 2623(1):73–81.
Böhm, A. and Kunert, K. (2016). Data age based mac scheme for fast and reliable communication within and between platoons of vehicles. In 2016 IEEE 12th (WiMob), pages 1–9.
Chen, Q., Tang, S., Yang, Q., and Fu, S. (2019). Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds. In 2019 IEEE 39th ICDCS, pages 514–524.
Choi, H. et al. (2018). Detecting attacks against robotic vehicles: A control invariant approach. CCS ’18, page 801–816, New York, NY, USA.
Choi, H. et al. (2020). Software-based realtime recovery from sensor attacks on robotic vehicles. In 23rd RAID 2020, pages 349–364, San Sebastian. USENIX Association.
Fakhfakh, F., Tounsi, M., and Mosbah, M. (2020). Vehicle platooning systems: Review, classification and validation strategies. IJNDC, 8:203–213.
Fernandes, P. and Nunes, U. (2012). Platooning with ivc-enabled autonomous vehicles: Strategies to mitigate communication delays, improve safety and traffic flow. IEEE Transactions on Intelligent Transportation Systems, 13(1):91–106.
He, Q. et al. (2020). Towards a severity assessment method for potential cyber attacks to connected and autonomous vehicles. Journal of Advanced Transportation, 2020:6873273.
Junejo, K. N. and Goh, J. (2016). Behaviour-based attack detection and classification in cyber physical systems using machine learning. In 2nd ACM Interntional Workshop on CPSS, page 34–43, New York, NY, USA.
Khan, M. A. et al. (2022). Level-5 autonomous driving—are we there yet? a review of research literature. ACM Comput. Surv., 55(2).
Ko, B. and Son, S. H. (2021). An approach to detecting malicious information attacks for platoon safety. IEEE Access, 9:101289–101299.
Maiti, S., Winter, S., Kulik, L., and Sarkar, S. (2020). The impact of flexible platoon formation operations. IEEE Transactions on Intelligent Vehicles, 5(2):229–239.
Mitchell, R. et al. (2015). Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems. IEEE Trans. on Depend. and Secure Computing, 12(1):16–30.
Morando, M. M. et al. (2018). Studying the safety impact of autonomous vehicles using simulation-based surrogate safety measures. Journal of Advanced Transportation, 2018.
Mushtaq, A., Haq, I. u., Nabi, W. u., Khan, A., and Shafiq, O. (2021). Traffic flow management of autonomous vehicles using platooning and collision avoidance strategies. Electronics, 10(10).
Onishi, H. (2018). A survey: Engineering challenges to implement vanet security. In 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pages 1–6.
Rana, M. M. and Hossain, K. (2021). Connected and autonomous vehicles and infrastructures: A literature review. International Journal of Pavement Research and Technology.
Shoukry, Y. et al. (2013). Non-invasive spoofing attacks for anti-lock braking systems. In Cryptographic Hardware and Embedded Systems CHES 2013, pages 55–72, Berlin, Heidelberg.
Sun, G. et al. (2021). Strategic mitigation against wireless attacks on autonomous platoons. In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, pages 69–84, Cham. Springer International Publishing.
Tippenhauer, N. O. et al. (2011). On the requirements for successful gps spoofing attacks. In Proceedings of the 18th ACM CCCS, CCS ’11, page 75–86, New York, NY, USA.
Trippel, T. et al. (2017). Walnut: Waging doubt on the integrity of mems accelerometers with acoustic injection attacks. In 2017 IEEE EuroS P, pages 3–18.
Yang, T. and Lv, C. (2021). A secure sensor fusion framework for connected and automated vehicles under sensor attacks. IEEE Internet of Things Journal, pages 1–1.
Yao, S. et al. (2020). Managing connected automated vehicles in mixed traffic considering communication reliability: a platooning strategy. Transportation Research Procedia, 47:43–50. 22nd EWGT 2019, Barcelona, Spain.
Publicado
23/05/2022
Como Citar
ANDRADE, Everaldo; SANTOS, Aldri.
Recuperação do Serviço de Pelotão de Veículos Autônomos e Conectados Baseado em Modelos Virtuais Contra Ataques na Rede e Sensores. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 531-544.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2022.222366.
