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Performance Analysis of Consensus-Based Distributed System Under False Data Injection Attacks

  • Xiaoyan Zheng
  • Lei Xie
  • Huifang ChenEmail author
  • Chao Song
Conference paper
  • 166 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 312)

Abstract

This paper investigates the security problem of consensus-based distributed system under false data injection attacks (FDIAs). Since the injected false data will spread to the whole network through data exchange between neighbor nodes, and result in continuing effect on the system performance, it is significant to study the impact of the attack. In this paper, we consider two attack models according to the property of the injection data, the deterministic attack and the stochastic attack. Then, the necessary and sufficient condition for the convergence of distributed system under the attack are derived, and the attack feature making the system unable to converge is provided. Moreover, the convergence result under resource-limited attack is deviated. On the other hand, the statistical properties of the convergence performance under zero-mean and non-zero-mean stochastic attacks are analyzed, respectively. Simulation results illustrate the effects caused by FDIAs on the convergence performance of distributed system.

Keywords

Consensus-based distributed system False data injection attack (FDIA) Performance analysis Convergence 

References

  1. 1.
    Pasqualetti, F., Bicchi, A., Bullo, F.: Consensus computation in unreliable networks: a system theoretic approach. IEEE Trans. Autom. Control 57(1), 90–104 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Kar, S., Moura, J.M.F.: Consensus + innovations distributed inference over networks: cooperation and sensing in networked systems. IEEE Signal Process. Mag. 30(3), 99–109 (2013)CrossRefGoogle Scholar
  3. 3.
    Zhang, W., Wang, Z., Guo, Y., Liu, H., Chen, Y., Mitola III, J.: Distributed cooperative spectrum sensing based on weighted average consensus. In: Proceedings of IEEE GLOBECOM, Houston, TX, USA, pp. 1–6 (2011)Google Scholar
  4. 4.
    Olfati-Saber, R., Murray, R.M.: Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control 49(9), 1520–1533 (2004)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kailkhura, B., Brahma, S., Varshney, P.K.: Data falsification attacks on consensus-based detection systems. IEEE Trans. Signal Inf. Process. Netw. 3(1), 145–158 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Yan, Q., Li, M., Jiang, T., Lou, W., Hou, Y.T.: Vulnerability and protection for distributed consensus-based spectrum sensing in cognitive radio networks. In: Proceedings of IEEE INFOCOM, Orlando, FL, USA, pp. 900–908 (2012)Google Scholar
  7. 7.
    He, J., Zhou, M., Cheng, P., Shi, L., Chen, J.: Consensus under bounded noise in discrete network systems: an algorithm with fast convergence and high accuracy. IEEE Trans Cybern. 46(12), 2874–2884 (2016)CrossRefGoogle Scholar
  8. 8.
    Jadbabaie, A., Olshevsky, A.: On performance of consensus protocols subject to noise: role of hitting times and network structure. In: Proceedings of 2016 IEEE CDC, Las Vegas, NV, pp. 179–184 (2016)Google Scholar
  9. 9.
    Jadbabaie, A., Olshevsky, A.: Scaling laws for consensus protocols subject to noise. IEEE Trans. Autom. Control 64(4), 1389–1402 (2019)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Aysal, T.C., Barner, K.E.: Convergence of consensus models with stochastic disturbances. IEEE Trans. Inf. Theory 56(8), 4101–4113 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Yang, Y., Blum, R.S.: Broadcast-based consensus with non-zero-mean stochastic perturbations. IEEE Trans. Inf. Theory 59(6), 3971–3989 (2013)CrossRefGoogle Scholar
  12. 12.
    Meng, D., Moore, K.L.: Studies on resilient control through multiagent consensus networks subject to disturbances. IEEE Trans Cybern. 44(11), 2050–2064 (2014)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  • Xiaoyan Zheng
    • 1
  • Lei Xie
    • 1
    • 2
  • Huifang Chen
    • 1
    • 3
    Email author
  • Chao Song
    • 1
  1. 1.College of Information Science and Electronic EngineeringZhejiang UniversityHangzhouChina
  2. 2.Zhejiang Provincial Key Laboratory of Information Processing, Communication and NetworkingHangzhouChina
  3. 3.Zhoushan Ocean Research CenterZhoushanChina

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