Advertisement

Map-Based Localization Under Adversarial Attacks

  • Yulin YangEmail author
  • Guoquan Huang
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

Due to increasing proliferation of autonomous vehicles, securing robot navigation against malicious attacks becomes a matter of urgent societal interest, because attackers can fool these vehicles by manipulating their sensors, exposing us to unprecedented vulnerabilities and ever-increasing possibilities for malicious attacks. To address this issue, we analyze in-depth the Maximum Correntropy Criterion Extended Kalman Filter (MCC-EKF) and propose a weighted MCC-EKF (WMCC-EKF) algorithm by systematically, rather than in an ad-hoc way, inflating the noise covariance of the compromised measurements based on each measurement’s quality. As a conservative alternative, we also design a secure estimator by first detecting attacks based on \(\ell _0 (\ell _1)\)-optimization assuming that only a small number of measurements can be attacked, and then employ a sliding-window Kalman filter to update the state estimates and covariance using only the uncompromised measurements—the resulting algorithm is termed Secure Estimation-EKF (SE-EKF). Both Monte-Carlo simulations and experiments are performed to validate the proposed secure estimators for map-based localization.

Notes

Acknowledgements

This work was partially supported by the University of Delaware College of Engineering, UD Cybersecurity Initiative, the Delaware NASA/EPSCoR Seed Grant, the NSF (IIS-1566129), and the DTRA (HDTRA1-16-1-0039).

References

  1. 1.
    Harris, M.: Researcher hacks self-driving car sensors. IEEE Spectrum (2015)Google Scholar
  2. 2.
    Charette, R.N.: Commercial drones and GPS spoofers a bad mix. IEEE Spectrum (2012)Google Scholar
  3. 3.
    Pasqualetti, F., Dörfler, F., Bullo, F.: Attack detection and identification in cyber-physical systems. IEEE Trans. Autom. Control. 58(11), 2715–2729 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Fawzi, H., Tabuada, P., Diggavi, S.: Secure estimation and control for cyber-physical systems under adversarial attacks. IEEE Trans. Autom. Control. 59, 1454–1467 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Mo, Y., Sinopoli, B.: Secure estimation in the presence of integrity attacks. IEEE Trans. Autom. Control. 60(4), 1145–1151 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Pajic, M., Weimer, J., Bezzo, N., Tabuada, P., Sokolsky, O., Lee, I., Pappas, G.: Robustness of attack-resilient state estimators. In: Proceedings of the ACM/IEEE Conference on Cyber-Physical Systems, pp. 163–174 (2014)Google Scholar
  7. 7.
    Shoukry, Y., Puggelli, A., Nuzzo, P., Sangiovanni-Vincentelli, A.L., Seshia, S.A., Tabuada, P.: Sound and complete state estimation for linear dynamical systems under sensor attacks using satisfiability modulo theory solving. In: American Control Conference, pp. 3818–3823. IEEE (2015)Google Scholar
  8. 8.
    Mo, Y., Murray, R.M.: Multi-dimensional state estimation in adversarial environment. In: Proceedings of the Chinese Control Conference, Hangzhou, China, pp. 28–30 (2015)Google Scholar
  9. 9.
    Langner, R.: Stuxnet: dissecting a cyberwarfare weapon. IEEE Secur. Priv. 9, 49–51 (2011)CrossRefGoogle Scholar
  10. 10.
    Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. 14, 1–33 (2011)CrossRefGoogle Scholar
  11. 11.
    Rutkin, A.H.: Spoofers use fake GPS signals to knock a yacht off course, Aug 2013. http://www.udel.edu/003938
  12. 12.
    Pajic, M., Tabuada, P., Lee, I., Pappas, G.J.: Attack-resilient state estimation in the presence of noise. In: Conference on Decision and Control, pp. 5827–5832. IEEE (2015)Google Scholar
  13. 13.
    Pajic, M., Lee, I., Pappas, G.J.: Attack-resilient state estimation for noisy dynamical systems. IEEE Trans. Control. Netw. Syst. 4(1), 82–92 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Chong, M.S., Wakaiki, M., Hespanha, J.P.: Observability of linear systems under adversarial attacks. In: American Control Conference, pp. 2439–2444. IEEE (2015)Google Scholar
  15. 15.
    Bezzo, N., Weimer, J., Pajic, M., Sokolsky, O., Pappas, G.J., Lee, I.: Attack resilient state estimation for autonomous robotic systems. In: Proceedings of IEEE Conference on Intelligent Robots and Systems, pp. 3692–3698. IEEE (2014)Google Scholar
  16. 16.
    Hu, Q., Chang, Y.H., Tomlin, C.J.: Secure estimation for unmanned aerial vehicles against adversarial cyber attacks (2016). arXiv:1606.04176
  17. 17.
    Candes, E.J., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Shoukry, Y., Nuzzo, P., Bezzo, N., Sangiovanni-Vincentelli, A., Seshia, S.A., Tabuada, P.: Attack detection and state reconstruction in differentially flat systems under sensor attacks using satisfiability modulo theory solving. In: Conference on Decision and Control, Osaka, Japan, pp. 15–18 (2015)Google Scholar
  19. 19.
    Izanloo, R., Fakoorian, S.A., Yazdi, H.S., Simon, D.: Kalman filtering based on the maximum correntropy criterion in the presence of non-gaussian noise. In: Conference on Information Science and Systems (CISS), pp. 500–505 (2016)Google Scholar
  20. 20.
    Liu, X., Qu, H., Zhao, J., Chen, B.: Extended kalman filter under maximum correntropy criterion. In: International Joint Conference on Neural Networks, pp. 1733–1737 (2016)Google Scholar
  21. 21.
    Yang, Y., Huang, G.: Map-based localization under adversarial attacks,” Tech. Rep. 2017-003, University of Delaware, Department of Mechanical Engineering, Oct 2017. Link: udel.edu/\(\sim \)ghuang/papers/tr\({}\_\)secure.pdfGoogle Scholar
  22. 22.
    Kulikova, M.: Square-root algorithms for maximum correntropy estimation of linear discrete-time systems in presence of non-gaussian noise (2016). arXiv:1610.00257
  23. 23.
    Chang, Y.H., Hu, Q., Tomlin, C.J.: Secure estimation based kalman filter for cyber-physical systems against adversarial attacks. arXiv:1512.03853
  24. 24.
    Roumeliotis, S.I., Burdick, J.W.: Stochastic cloning: a generalized framework for processing relative state measurements. In: Proceedings of IEEE Conference on Robotics and Automation, Washington, DC, pp. 1788–1795, May 11–15 2002Google Scholar
  25. 25.
    Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale \(l_1\)-regularized least squares. IEEE J. Sel. Top. Signal Process. 1, 606–617 (2007)CrossRefGoogle Scholar
  26. 26.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. Wiley (2004)Google Scholar
  27. 27.
    Guivant, J.E., Nebot, E.M.: Optimization of the simultaneous localization and map building algorithm for real time implementation. IEEE Trans. Robot. Autom. 17, 242–257 (2001)CrossRefGoogle Scholar
  28. 28.
    Dellaert, F.: Factor graphs and gtsam: a hands-on introduction. Technical report (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of DelawareNewarkUSA

Personalised recommendations