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REDEM: Real-Time Detection and Mitigation of Communication Attacks in Connected Autonomous Vehicle Applications

  • Srivalli BoddupalliEmail author
  • Sandip Ray
Conference paper
  • 28 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 574)

Abstract

Emergent vehicles will support a variety of connected applications, where a vehicle communicates with other vehicles or with the infrastructure to make a variety of decisions. Cooperative connected applications provide a critical foundational pillar for autonomous driving, and hold the promise of improving road safety, efficiency and environmental sustainability. However, they also induce a large and easily exploitable attack surface: an adversary can manipulate vehicular communications to subvert functionality of participating individual vehicles, cause catastrophic accidents, or bring down the transportation infrastructure. In this paper we outline a potential direction to address this critical problem through a resiliency framework, REDEM, based on machine learning. REDEM has several interesting features, including (1) smooth integration with the architecture of the underlying application, (2) ability to handle diverse communication attacks within the same underlying foundation, and (3) real-time detection and mitigation capability. We present the vision of REDEM, identify some key challenges to be addressed in its realization, and discuss the kind of evaluation/analysis necessary for its viability. We also present initial results from one instantiation of REDEM introducing resiliency in Cooperative Adaptive Cruise Control (CACC).

Keywords

Vehicular communication Automotive security Machine learning Anomaly detection 

References

  1. 1.
  2. 2.
    Abdollahi Biron, Z., Dey, S., Pisu, P.: Real-time detection and estimation of denial of service attack in connected vehicle systems. IEEE Trans. Intell. Transp. Syst. 19(12), 3893–3902 (2018)CrossRefGoogle Scholar
  3. 3.
    Akhtar, N., Mian, A.: Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access 6, 1441–14430 (2018)Google Scholar
  4. 4.
    Alheeti, K.M.A., Al-Ani, M.S., McDonald-Maier, K.: A hierarchical detection method in external communication for self-driving vehicles based on TDMA. PLoS ONE 13(1), e0188760 (2018)CrossRefGoogle Scholar
  5. 5.
    Amoozadeh, M., Deng, H., Chuah, C.-N., Zhang, H.M., Ghosal, D.: Platoon management with cooperative adaptive cruise control enabled by VANET. Veh. Commun. 2(2), 110–123 (2015)Google Scholar
  6. 6.
    Aygun, B., Lin, C.-W., Shiraishi, S., Wyglinski, A.: Selective message relaying for multi-hopping vehicular networks. In: IEEE Vehicular Networking Conference, pp. 1–8 (2016)Google Scholar
  7. 7.
    Aygun, B., Lin, C.-W., Shiraishi, S., Wyglinski, A.M.: Selective message relaying for multi-hopping vehicular networks. In: 2016 IEEE Vehicular Networking Conference (VNC), pp. 1–8. IEEE (2016)Google Scholar
  8. 8.
    Bergenhem, C., Pettersson, H., Coelingh, E., Englund, C., Shladover, S., Tsugawa, S.: Overview of platooning systems. In: 19th ITS World Congress (2012)Google Scholar
  9. 9.
    Bergenhem, C., Shladover, S., Coelingh, E., Englund, C., Tsugawa, S.: Overview of platooning systems. In: Proceedings of the 19th ITS World Congress, Vienna, Austria, 22–26 October 2012 (2012)Google Scholar
  10. 10.
    Berger, I., Rieke, R., Kolomeets, M., Chechulin, A., Kotenko, I.: Comparative study of machine learning methods for in-vehicle intrusion detection. In: Katsikas, S.K., et al. (eds.) SECPRE/CyberICPS -2018. LNCS, vol. 11387, pp. 85–101. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-12786-2_6CrossRefGoogle Scholar
  11. 11.
    Checkoway, S., et al.: Comprehensive experimental analyses of automotive attack surfaces. In: USENIX Security Symposium, San Francisco, vol. 4 (2011)Google Scholar
  12. 12.
    Du, L., Chen, S., Han, L.: Coordinated online in-vehicle navigation guidance based on routing game theory. Transp. Res. Rec.: J. Transp. Res. Board 2497, 106–116 (2015)CrossRefGoogle Scholar
  13. 13.
    Du, L., Chen, S., Han, L.: Coordinated online in-vehicle navigation guidance based on routing game theory. Transp. Res. Rec. 2497(1), 106–116 (2015)CrossRefGoogle Scholar
  14. 14.
    Du, L., Han, L., Li, X.: Distributed coordinated in-vehicle online routing under mixed strategy congestion game. Transp. Res. Part B: Methodol. 67, 235–252 (2014)CrossRefGoogle Scholar
  15. 15.
    Dutta, R.G., Yu, F., Zhang, T., Hu, Y., Jin, Y.: Security for safety: a path toward building trusted autonomous vehicles. In: 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1–6, November 2018Google Scholar
  16. 16.
    Dutta, R.G., Yu, F., Zhang, T., Hu, Y., Jin, Y.: Security for safety: a path toward building trusted autonomous vehicles. In: Proceedings of the International Conference on Computer-Aided Design, p. 92. ACM (2018)Google Scholar
  17. 17.
    Hempfield, C.: Why a Cybersecurity Solution for Driverless Cars May be Found Under the Hood (2017). https://techcrunch.com/2017/02/18/why-a-cybersecurity-solution-for-driverless-cars-may-be-found-under-the-hood
  18. 18.
    Jagielski, M., Jones, N., Lin, C., Nita-Rotaru, C., Shiraishi, S.: Threat detection in collaborative adaptive cruise control in connected cars. In: WISEC, pp. 184–189 (2018)Google Scholar
  19. 19.
    Jagielski, M., Jones, N., Lin, C.-W., Nita-Rotaru, C., Shiraishi, S.: Threat detection for collaborative adaptive cruise control in connected cars. In: Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks, pp. 184–189. ACM (2018)Google Scholar
  20. 20.
    Kang, M.-J., Kang, J.-W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6), e0155781 (2016)CrossRefGoogle Scholar
  21. 21.
    Koscher, K., et al.: Experimental security analysis of a modern automobile. In: 2010 IEEE Symposium on Security and Privacy, pp. 447–462. IEEE (2010)Google Scholar
  22. 22.
    Levi, M., Allouche, Y., Kontorovich, A.: Advanced analytics for connected car cybersecurity. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pp. 1–7 (2018)Google Scholar
  23. 23.
    Lin, Y.-T., Hsu, H., Lin, S.-C., Lin, C.-W., Jiang, I.H.-R., Liu, C.: Graph-based modeling, scheduling, and verification for intersection management of intelligent vehicles. ACM Trans. Embed. Comput. Syst. (TECS) 18(5s), 95 (2019)Google Scholar
  24. 24.
    Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv, abs/1706.06083 (2018)Google Scholar
  25. 25.
    Miller, C., Valasek, C.: A survey of remote automotive attack surfaces. Black Hat USA 2014, p. 94 (2014)Google Scholar
  26. 26.
    Miller, C., Valasek, C.: Remote exploitation of an unaltered passenger vehicle. Black Hat USA 2015, p. 91 (2015)Google Scholar
  27. 27.
    National Highway Traffic Safety Association. Road Accidents in USA. https://www.recalls.gov/nhtsa.html
  28. 28.
    Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: IEEE European Symposium on Security and Privacy (2016)Google Scholar
  29. 29.
    Sayin, M.O., Lin, C.-W., Shiraishi, S., Shen, J., Basar, T.: Information-driven autonomous intersection control via incentive compatible mechanisms. IEEE Trans. Intell. Transp. Syst. 20(3), 912–924 (2019)CrossRefGoogle Scholar
  30. 30.
    Tian, Y., Pei, K., Jana, S., Ray, B.: Deeptest: automated testing of deep-neural-network-driven autonomous cars. In: Proceedings of the 40th International Conference on Software Engineering, ICSE 2018, pp. 303–314 (2018)Google Scholar
  31. 31.
    Tiwari, A., et al.: Safety envelope for security. In: Proceedings of the 3rd International Conference on High Confidence Networked Systems, HiCoNS 2014, pp. 85–94 (2014)Google Scholar
  32. 32.
    Uricár, M., Krízek, P., Hurych, D., Sobh, I., Yogamani, S., Denny, P.: Yes, we GAN: applying adversarial techniques for autonomous driving. CoRR, abs/1902.03442 (2019)Google Scholar
  33. 33.
    Zhang, H., Chen, H., Song, Z., Boning, D., Dhillon, I., Hsieh, C.-J.: The limitations of adversarial training and the blind-spot attack. In: International Conference on Learning Representations (2019)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of Florida at GainesvilleGainesvilleUSA

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