Gerenciamento de Tráfego Seguro para Redes VANETs na Presença de Ataques de Envenenamento de Dados
Resumo
Os sistemas inspirados em redes veiculares geralmente processam um grande volume de dados numa demanda de tempo real, sendo sensíveis a ataques que podem comprometer seu funcionamento ou mesmo interrompê-los. Os ataques de Envenenamento de Dados (EvD) destacam-se dentre os mais danosos por alterar a confiabilidade dos dados disseminados. Embora existam mecanismos para lidar com essas ameaças, a validação dos dados e a detecção colaborativa sobre os serviços de VANETs são frequentemente desconsideradas. Este trabalho propõe um sistema de gerenciamento de tráfego em VANETs eficiente e seguro contra ataques de EvD, chamado RONDA. O sistema possui um mecanismo que emprega monitoramento watchdog e consenso relacional para detecção de atacantes, assegurando a autenticidade e disponibilidade dos dados. O RONDA foi avaliado no OMNET++, junto com o sistema ON-DEMAND, e obteve 90% de taxa de detecção, 4% de falsos negativos e 10% de falsos positivos, e diminuiu em até 40% o tempo de viagem dos veículos congestionados.Referências
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Kamel, J., Ansari, M. R., Petit, J., Kaiser, A., Jemaa, I. B., and Urien, P. (2020). Simulation framework for misbehavior detection in vehicular networks. IEEE Transactions on Vehicular Technology, 69(6):6631–6643.
Khan, M. A., Sheikh, M. S., and Liang, J. (2019). A comprehensive survey on vanet security services in traffic management system. Wireless Communications and Mobile Computing, 2019:1 – 23.
Li, B., Lu, R., Wang, W., and Choo, K.-K. R. (2017). Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. Journal of Parallel and Distributed Computing, 103:32–41.
Lima, M. N., dos Santos, A. L., and Pujolle, G. (2009). A survey of survivability in mobile ad hoc networks. IEEE Communications Surveys and Tutorials, 11(1):66–77.
Lu, Z., Qu, G., and Liu, Z. (2019). A survey on recent advances in vehicular network security, trust, and privacy. IEEE Trans. on Intelligent Transp. Sys., 20(2):760–776.
Pan, J., Popa, I. S., and Borcea, C. (2017). Divert: A distributed vehicular traffic re-routing system for congestion avoidance. IEEE Trans. on Mobile Computing, 16(1):58–72.
Pedroso, C., Gielow, F., Santos, A., and Nogueira, M. (2019). Mitigação de Ataques IDFs no Serviço de Agrupamento de Disseminação de Dados em Redes IoT Densas. In Anais SBSeg 2019, Porto Alegre, RS, Brasil. SBC.
Santos, A. L., Cervantes, C. A., Nogueira, M., and Kantarci, B. (2019). Clustering and reliability-driven mitigation of routing attacks in massive iot systems. JISA, 10(1):18.
Sen, A. and Madria, S. (2017). Risk assessment in a sensor cloud framework using attack graphs. IEEE Transactions on Services Computing, 10(6):942–955.
Soriano, F. R., Samper-Zapater, J. J., Martinez-Dura, J. J., Cirilo-Gimeno, R. V., and Martinez Plume, J. (2018). Smart mobility trends: Open data and other tools. IEEE Intelligent Transportation Systems Magazine, 10(2):6–16.
Thakur, A. and Malekian, R. (2019). Fog computing for detecting vehicular congestion, IEEE Intelligent Transportation an internet of vehicles based approach: A review. Systems Magazine, 11(2):8–16.
Toulouse, M., Minh, B. Q., and Curtis, P. (2015). A consensus based network intrusion detection system. In 2015 5th International Conference on IT Convergence and Security (ICITCS), pages 1–6. IEEE.
Wang, S., Djahel, S., Zhang, Z., and Mcmanis, J. (2016). Next road rerouting: A multiagent system for mitigating unexpected urban traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 17:1–12.
Zhang, C., Chen, K., Zeng, X., and Xue, X. (2018). Misbehavior detection based on support vector machine and dempster-shafer theory of evidence in vanets. IEEE Access, 6:59860 – 59870.
Deng, R., Xiao, G., Lu, R., Liang, H., and Vasilakos, A. V. (2016). False data injection on state estimation in power systems—attacks, impacts, and defense: A survey. IEEE Trans. on Industrial Informatics, 13(2):411–423.
Gomides, T. S., De Grande, R. E., de Souza, A. M., Souza, F. S., Villas, L. A., and Guidoni, D. L. (2020). An adaptive and distributed traffic management system using vehicular ad-hoc networks. Computer Communications, 159:317 – 330.
Guidoni, D. L., Maia, G., Souza, F. S. H., Villas, L. A., and Loureiro, A. A. F. (2020). Vehicular traffic management based on traffic engineering for vehicular ad hoc networks. IEEE Access, pages 45167–45183.
Kamel, J., Ansari, M. R., Petit, J., Kaiser, A., Jemaa, I. B., and Urien, P. (2020). Simulation framework for misbehavior detection in vehicular networks. IEEE Transactions on Vehicular Technology, 69(6):6631–6643.
Khan, M. A., Sheikh, M. S., and Liang, J. (2019). A comprehensive survey on vanet security services in traffic management system. Wireless Communications and Mobile Computing, 2019:1 – 23.
Li, B., Lu, R., Wang, W., and Choo, K.-K. R. (2017). Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. Journal of Parallel and Distributed Computing, 103:32–41.
Lima, M. N., dos Santos, A. L., and Pujolle, G. (2009). A survey of survivability in mobile ad hoc networks. IEEE Communications Surveys and Tutorials, 11(1):66–77.
Lu, Z., Qu, G., and Liu, Z. (2019). A survey on recent advances in vehicular network security, trust, and privacy. IEEE Trans. on Intelligent Transp. Sys., 20(2):760–776.
Pan, J., Popa, I. S., and Borcea, C. (2017). Divert: A distributed vehicular traffic re-routing system for congestion avoidance. IEEE Trans. on Mobile Computing, 16(1):58–72.
Pedroso, C., Gielow, F., Santos, A., and Nogueira, M. (2019). Mitigação de Ataques IDFs no Serviço de Agrupamento de Disseminação de Dados em Redes IoT Densas. In Anais SBSeg 2019, Porto Alegre, RS, Brasil. SBC.
Santos, A. L., Cervantes, C. A., Nogueira, M., and Kantarci, B. (2019). Clustering and reliability-driven mitigation of routing attacks in massive iot systems. JISA, 10(1):18.
Sen, A. and Madria, S. (2017). Risk assessment in a sensor cloud framework using attack graphs. IEEE Transactions on Services Computing, 10(6):942–955.
Soriano, F. R., Samper-Zapater, J. J., Martinez-Dura, J. J., Cirilo-Gimeno, R. V., and Martinez Plume, J. (2018). Smart mobility trends: Open data and other tools. IEEE Intelligent Transportation Systems Magazine, 10(2):6–16.
Thakur, A. and Malekian, R. (2019). Fog computing for detecting vehicular congestion, IEEE Intelligent Transportation an internet of vehicles based approach: A review. Systems Magazine, 11(2):8–16.
Toulouse, M., Minh, B. Q., and Curtis, P. (2015). A consensus based network intrusion detection system. In 2015 5th International Conference on IT Convergence and Security (ICITCS), pages 1–6. IEEE.
Wang, S., Djahel, S., Zhang, Z., and Mcmanis, J. (2016). Next road rerouting: A multiagent system for mitigating unexpected urban traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 17:1–12.
Zhang, C., Chen, K., Zeng, X., and Xue, X. (2018). Misbehavior detection based on support vector machine and dempster-shafer theory of evidence in vanets. IEEE Access, 6:59860 – 59870.
Publicado
13/10/2020
Como Citar
PEDROSO, Carlos; GOMIDES, Thiago S.; GUIDONI, Daniel L.; SANTOS, Aldri.
Gerenciamento de Tráfego Seguro para Redes VANETs na Presença de Ataques de Envenenamento de Dados. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 20. , 2020, Petrópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 299-312.
DOI: https://doi.org/10.5753/sbseg.2020.19245.