• DocumentCode
    116025
  • Title

    Coding sensor outputs for injection attacks detection

  • Author

    Fei Miao ; Quanyan Zhu ; Pajic, Miroslav ; Pappas, George J.

  • Author_Institution
    Dept. of Electr. & Syst. Eng., Univ. of Pennsylvania, Philadelphia, PA, USA
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    5776
  • Lastpage
    5781
  • Abstract
    This paper considers a method of coding the sensor outputs in order to detect stealthy false data injection attacks. An intelligent attacker can design a sequence of data injection to sensors that pass the state estimator and statistical fault detector, based on knowledge of the system parameters. To stay undetected, the injected data should increase the state estimation errors while keep the estimation residues in a small range. We employ a coding matrix to the original sensor outputs to increase the estimation residues, such that the alarm will be triggered by the detector even under intelligent data injection attacks. This is a low cost method compared with encryption over sensor communication networks. We prove the conditions the coding matrix should satisfy under the assumption that the attacker does not know the coding matrix yet. An iterative optimization algorithm is developed to compute a feasible coding matrix, and, we show that in general, multiple feasible coding matrices exist.
  • Keywords
    computer crime; cryptography; fault diagnosis; iterative methods; matrix algebra; optimisation; state estimation; statistical analysis; coding matrix; coding sensor outputs; encryption; estimation residues; intelligent attacker; intelligent data injection attacks; iterative optimization algorithm; sensor communication networks; state estimation errors; statistical fault detector; stealthy false data injection attacks detection; system parameters; Detectors; Encoding; Estimation error; Kalman filters; Monitoring; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7040293
  • Filename
    7040293