Gerenciamento de Tráfego Seguro para Redes VANETs na Presença de Ataques de Envenenamento de Dados

• Carlos Pedroso UFPR
• Thiago S. Gomides UFSJ
• Daniel L. Guidoni UFSJ
• Aldri Santos UFPR

DOI: https://doi.org/10.5753/sbseg.2020.19245

Abstract

Systems inspired by vehicular networks generally process a large volume of data on real-time demand, being sensitive to attacks that can compromise their functioning or even interrupt them. Data Poisoning attacks (AdP) stand out among the most damaging by changing the reliability of the disseminated data. Although mechanisms exist to deal with these threats, data validation, and collaborative detection on VANETs services are often overlooked. This paper proposes an efficient and secure VANETs traffic management system against EvD attacks, called RONDA. The system has a mechanism that uses watchdog monitoring and relational consensus to detect attackers, ensuring the authenticity and availability of the data. It was evaluated on OMNET++, compared to ON-DEMAND, and attained 90% of detection rate, 4% of false negative and 10% of false positive rates, and decreased vehicle travel time by up to 40%.

References

Arif, M., Wang, G., Zakirul Alam Bhuiyan, M., Wang, T., and Chen, J. (2019). A survey on security attacks in vanets: Communication, applications and challenges. Vehicular Communications, 19:100179.

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.