access icon free DoS attack in centralised sensor network against state estimation

This study considers a model that a centralised sensor network is attacked by an invader, who launches the denial-of-service (DoS) attack on the network. To understand the behaviour of the invader and propose necessary protection accordingly, the authors study how the invader optimises his attack. In the model, the sensors take a measurement of a process and send the measurements to a remote estimator for state estimation. The invader intends to block the communication channels from the sensors to the estimator by the DoS attack in order to degrade the estimation performance. Constrained by a power budget, the invader needs to decide which sensors and at which time instances to attack, with the target that the estimation performance is mostly deteriorated. In this study, two scenarios that the system has a single sensor and has multiple sensors are investigated, respectively. For the system with a single sensor, the analytical result of the optimal attack schedules is given. For the system with multiple sensors, numerical methods are proposed, where the problem is relaxed and transformed into a convex optimisation problem which can be solved by efficient numerical algorithms.

Inspec keywords: optimisation; telecommunication security; convex programming; wireless channels; state estimation; scheduling; wireless sensor networks; computer network security; estimation theory; jamming; numerical analysis; networked control systems

Other keywords: denial-of-service attack; multiple sensors; state estimation; estimation performance; invader; single sensor; optimal attack schedules; authors study; remote estimator; centralised sensor network; DoS attack

Subjects: Optimisation techniques; Data security; Computer communications; Optimisation techniques

References

    1. 1)
      • 3. Liu, Y., Ning, P., Reiter, M.K.: ‘False data injection attacks against state estimation in electric power grids’. Proc. 16th ACM Conf. on Computer and Communications Security, Chicago, Illinois, USA, 2009, pp. 2132.
    2. 2)
      • 4. Kosut, O., Jia, L., Thomas, R.J., et al: ‘Limiting false data attacks on power system state estimation’. Proc. 44th Annual Conf. on Information Sciences and Systems, Princeton, NJ, USA, 2010, pp. 16.
    3. 3)
      • 13. Zhang, H., Cheng, P., Shi, L., et al: ‘Optimal Denial-of-Service attack scheduling with energy constraint’, IEEE Trans. Autom. Control, 2015, 60, (11), pp. 30233028.
    4. 4)
      • 18. Yang, C., Ren, X., Yang, W., et al: ‘Jamming attack in centralized state estimation’. Proc. 34th Chinese Control Conf., Hangzhou, China, 2015, pp. 65306535.
    5. 5)
      • 6. Mo, Y., Sinopoli, B.: ‘On the performance degradation of cyber-physical systems under stealthy integrity attacks’, IEEE Trans. Autom. Control, 2016, 61, (9), pp. 26182624.
    6. 6)
      • 7. Mo, Y., Sinopoli, B.: ‘Secure control against replay attacks’. Proc. 47th Annual Allerton Conf. on Communication, Control, and Computing, Allerton House, UIUC, Illinois, USA, 2009, pp. 911918.
    7. 7)
      • 1. Vijayan, J.: ‘Stuxnet renews power grid security concerns’. Computerworld, 2010, Available at: http://www.computerworld.com/article/2519574/security0/stuxnet-renews-power-grid-security-concerns.html.
    8. 8)
      • 10. Ding, D., Wei, G., Zhang, S., et al: ‘On scheduling of deception attacks for discrete-time networked systems equipped with attack detectors’, Neurocomputing, 2017, 219, pp. 99106.
    9. 9)
      • 14. Zhang, H., Cheng, P., Shi, L., et al: ‘Optimal DoS attack scheduling in wireless networked control system’, IEEE Trans. Control Syst. Technol., 2016, 24, (3), pp. 843852.
    10. 10)
      • 16. Guan, Y., Ge, X.: ‘Distributed attack detection and secure estimation of networked cyber-physical systems against false data injection attacks and jamming attacks’, IEEE Trans. Signal Inf. Process. Netw., 2018, 4, (1), pp. 4859.
    11. 11)
      • 11. Zhang, J., Blum, R.S., Lu, X., et al: ‘Asymptotically optimum distributed estimation in the presence of attacks’, IEEE Trans. Signal Process., 2015, 63, (5), pp. 10861101.
    12. 12)
      • 15. Li, Y., Shi, L., Cheng, P., et al: ‘Jamming attacks on remote state estimation in cyber physical systems: a game-theoretic approach’, IEEE Trans. Autom. Control, 2015, 60, (10), pp. 28312836.
    13. 13)
      • 2. Mo, Y., Kim, T.H.J., Brancik, K., et al: ‘Cyber-physical security of a smart grid infrastructure’, Proc. IEEE, 2012, 100, (1), pp. 195209.
    14. 14)
      • 19. Anderson, B., Moore, J.B.: ‘Optimal filtering’ (Prentice Hall, New Jersey, USA, 1979).
    15. 15)
      • 9. Ding, D., Wang, Z., Wei, G., et al: ‘Event-based security control for discrete-time stochastic systems’, IET Control Theory Applic., 2016, 10, (15), pp. 18081815.
    16. 16)
      • 8. Mo, Y., Weerakkody, S., Sinopoli, B.: ‘Physical authentication of control systems-designing watermarked control inputs to detect counterfeit sensor outputs’, IEEE Control Syst. Mag., 2015, 35, (1), pp. 93109.
    17. 17)
      • 12. Zhang, J., Blum, R.S., Kaplan, L.M., et al: ‘Functional forms of optimum spoofing attacks for vector parameter estimation in quantized sensor networks’, IEEE Trans. Signal Process., 2017, 65, (3), pp. 705720.
    18. 18)
      • 5. Mo, Y., Sinopoli, B.: ‘Robust estimation in the presence of integrity’. Proc. 42nd IEEE Conf. on Decision and Control, Florence, Italy, 2013, pp. 60856090.
    19. 19)
      • 17. Guan, Y., Ge, X.: ‘Distributed secure estimation over wireless sensor networks against random multichannel jamming attacks’, IEEE Access, 2017, 5, pp. 1085810870.
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