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A Bayesian Stackelberg Game Approach to Remote State Estimation Under SINR-Based DoS Attacks with Incomplete

Di Deng1,2, Peng Yi1,2, Mingze Qi3,4

  • 1Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.

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Summary
This summary is machine-generated.

This study introduces a Bayesian Stackelberg game to optimize sensor transmission and attacker interference strategies against denial-of-service (DoS) attacks, enhancing remote state estimation under energy constraints.

Keywords:
DoS attacksStackelberg gamecyber–physical systemsincomplete information

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Area of Science:

  • Cyber-Physical Security
  • Game Theory
  • Information Theory

Background:

  • Denial-of-Service (DoS) attacks pose significant threats to cyber-physical systems, particularly impacting remote state estimation.
  • Limited energy resources necessitate efficient transmission and interference strategies for sensors.
  • Incomplete information about attacker channel gains complicates defense mechanisms.

Purpose of the Study:

  • To investigate Stackelberg game strategies for a sensor and an attacker in remote state estimation under signal-to-interference-plus-noise ratio (SINR)-based DoS attacks.
  • To develop a method for balancing sensor estimation performance and energy consumption.
  • To address scenarios with incomplete information regarding the attacker's channel gain.

Main Methods:

  • Formulation of the scheduling problem as a Markov decision process over an infinite-time horizon.
  • Construction of a Bayesian Stackelberg game (BSG) incorporating probabilistic channel gain information.
  • Application of the best-response definition to solve the BSG and prove the existence of a Stackelberg equilibrium.
  • Utilization of a Stackelberg Q-learning algorithm to derive optimal strategies for both sensor and attacker.

Main Results:

  • The proposed Bayesian Stackelberg game effectively models the strategic interactions between the sensor and the attacker.
  • The Stackelberg Q-learning algorithm successfully identifies optimal transmission and interference strategies.
  • Numerical results validate the game method's effectiveness, even when the sensor lacks precise knowledge of the attacker's channel gain.

Conclusions:

  • The developed game-theoretic approach provides an effective framework for defending remote state estimation against DoS attacks under energy constraints and incomplete information.
  • The findings highlight the importance of adaptive strategies in cyber-physical security.
  • The study demonstrates a viable solution for enhancing system resilience in adversarial environments.