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Strategic Mitigation Against Wireless Attacks on Autonomous Platoons

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12978)


With the increased demand for connected and autonomous vehicles, vehicle platoons will play a significant role in the near future, enhancing traffic efficiency and safety. However, their reliance on wireless communication channels makes such systems susceptible to a range of cyber-attacks. An intelligent adversary could target the platoon through message falsification between vehicles to carry out high-impact attacks. This would create persistent degradation of platoon stability or even cause catastrophic collisions. In this paper, we present a novel, end-to-end attack detection and mitigation framework. We use a deep neural network as an example anomaly detector tuned to reduce false alarm rate. We then model the interactions between the imperfect detector, the intelligent adversary and the defense system as a non-cooperative security game with imperfect information. In this setting, the adversary performs a test-time boiling frog attack against the detector. The Nash-equilibrium solution considers the downstream effects of the test-time attack, to guide the control system reconfiguration for the vehicles to mitigate communication-based attacks. The simulations conducted in a sophisticated simulator demonstrate the potential for real-world online deployment in a distributed manner. Results show that our approach outperforms baseline methods by up to \(30\%\) in terms of increase of defense utilities, leading up to \(176\%\) increase in minimum inter-vehicle distances for collision avoidance under attacks.

We gratefully acknowledge support from the DSTG Next Generation Technology Fund and CSIRO Data61 CRP ‘Adversarial Machine Learning for Cyber’, and a CSIRO Data61 PhD scholarship.

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  1. Alotibi, F., Abdelhakim, M.: Anomaly detection for cooperative adaptive cruise control in autonomous vehicles using statistical learning and kinematic model. IEEE Trans. Intell. Transp. Syst. (2020)

    Google Scholar 

  2. Alpcan, T., Başar, T.: Network Security: A Decision and Game-Theoretic Approach. Cambridge University Press, Cambridge (2010)

    CrossRef  Google Scholar 

  3. Boeira, F., Barcellos, M.P., de Freitas, E.P., Vinel, A., Asplund, M.: Effects of colluding sybil nodes in message falsification attacks for vehicular platooning. In: 2017 IEEE Vehicular Networking Conference (VNC), pp. 53–60. IEEE (2017)

    Google Scholar 

  4. Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)

  5. Fang, F., et al.: Deploying paws: field optimization of the protection assistant for wildlife security. In: AAAI, vol. 16, pp. 3966–3973 (2016)

    Google Scholar 

  6. Khanapuri, E., Chintalapati, T., Sharma, R., Gerdes, R.: Learning-based adversarial agent detection and identification in cyber physical systems applied to autonomous vehicular platoon. In: 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS), pp. 39–45. IEEE (2019)

    Google Scholar 

  7. Lopez, P.A., et al.: Microscopic traffic simulation using sumo. In: The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE (2018).

  8. Lu, X.Y., Hedrick, J.K., Drew, M.: ACC/CACC-control design, stability and robust performance. In: Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301), vol. 6, pp. 4327–4332. IEEE (2002)

    Google Scholar 

  9. McKelvey, R.D., McLennan, A.M., Turocy, T.L.: Gambit: software tools for game theory (2006)

    Google Scholar 

  10. Merco, R., Biron, Z.A., Pisu, P.: Replay attack detection in a platoon of connected vehicles with cooperative adaptive cruise control. In: 2018 Annual American Control Conference (ACC), pp. 5582–5587. IEEE (2018)

    Google Scholar 

  11. Qayyum, A., Usama, M., Qadir, J., Al-Fuqaha, A.: Securing connected & autonomous vehicles: challenges posed by adversarial machine learning and the way forward. IEEE Commun. Surv. Tutor. 22(2), 998–1026 (2020)

    CrossRef  Google Scholar 

  12. Rajamani, R.: Vehicle Dynamics and Control. Springer, Heidelberg (2011)

    MATH  Google Scholar 

  13. Sedjelmaci, H., Senouci, S.M., Al-Bahri, M.: A lightweight anomaly detection technique for low-resource IoT devices: a game-theoretic methodology. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)

    Google Scholar 

  14. Subba, B., Biswas, S., Karmakar, S.: A game theory based multi layered intrusion detection framework for wireless sensor networks. Int. J. Wirel. Inf. Netw. 25(4), 399–421 (2018)

    CrossRef  Google Scholar 

  15. Sumra, I.A., Hasbullah, H.B., AbManan, J.B.: Attacks on security goals (confidentiality, integrity, availability) in VANET: a survey. In: Laouiti, A., Qayyum, A., Mohamad Saad, M.N. (eds.) Vehicular Ad-hoc Networks for Smart Cities. AISC, vol. 306, pp. 51–61. Springer, Singapore (2015).

    CrossRef  Google Scholar 

  16. Webots: Open-source Mobile Robot Simulation Software

  17. Wiedersheim, B., Ma, Z., Kargl, F., Papadimitratos, P.: Privacy in inter-vehicular networks: why simple pseudonym change is not enough. In: 2010 Seventh International Conference on Wireless on-Demand Network Systems and Services (WONS), pp. 176–183. IEEE (2010)

    Google Scholar 

  18. Yang, L., Moubayed, A., Hamieh, I., Shami, A.: Tree-based intelligent intrusion detection system in internet of vehicles. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2019)

    Google Scholar 

  19. Zhang, D., Shen, Y.P., Zhou, S.Q., Dong, X.W., Yu, L.: Distributed secure platoon control of connected vehicles subject to dos attack: theory and application. IEEE Trans. Syst. Man Cybern. Syst. (2020)

    Google Scholar 

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Sun, G., Alpcan, T., Rubinstein, B.I.P., Camtepe, S. (2021). Strategic Mitigation Against Wireless Attacks on Autonomous Platoons. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12978. Springer, Cham.

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  • Print ISBN: 978-3-030-86513-9

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