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Defending False Data Injection on State Estimation Over Fading Wireless Channels

Saptarshi Ghosh, Manav R. Bhatnagar, Walid Saad, Bijaya Ketan Panigrahi

2020IEEE Transactions on Information Forensics and Security20 citationsDOI

Abstract

In this paper, a cyber-physical system (CPS) is considered, whose state estimation is done by a central controller (CC) using the measurements received from a wireless powered sensor network (WPSN) over fading channels. An adversary injects false data in this system by compromising some of the idle sensor nodes (SNs) of the WPSN. Using the WPSN for transmitting supervision and control data, in the aforementioned setting, makes the CPS vulnerable to both error and false data injection (FDI). The existing techniques of launching stealthy FDI attack are not applicable to the aforementioned network due to the random nature of wireless channels, which is used for both transmitting control and false data. The objectives of the adversary and the CC to launch stealthy FDI attack and to detect the same, respectively, are found to be depending on the powers they use for transmitting data over wireless channels. The transmit powers of the CC, and the adversary that fulfill their respective objectives are derived by modeling their interaction as a Bayesian Stackelberg game. Based on their objectives, novel utility functions are defined for the CC and the adversary. Subsequently, the equilibrium of the proposed game is obtained by solving a non-convex bi-level quadratic-quadratic program. Finally, the analytical results are verified and compared with other state-of-art techniques by applying them in a realistic smart grid simulations.

Topics & Concepts

Computer scienceAdversaryStackelberg competitionWireless sensor networkWirelessFadingComputer networkChannel (broadcasting)Unexploded ordnanceState (computer science)Adversary modelWireless networkComputer securityReal-time computingTelecommunicationsAlgorithmMathematicsGeologyMathematical economicsRemote sensingSmart Grid Security and ResilienceSecurity in Wireless Sensor NetworksAdversarial Robustness in Machine Learning
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