Allocation of Eavesdropping Attacks for Multi-System Remote State Estimation.

Area of Science:

• Cyber-Physical Systems Security
• Wireless Communication Security
• Information Theory

Background:

• Cyber-physical systems (CPS) face increasing threats from eavesdropping attacks targeting remote state estimation.
• Optimizing eavesdropper strategies is crucial for understanding and mitigating these security vulnerabilities.
• Existing methods may lack efficiency in determining optimal attack parameters.

Purpose of the Study:

• To determine the optimal energy allocation strategy for an eavesdropper in a multi-system CPS.
• To maximize the state estimation error of a remote estimator under eavesdropping.
• To develop a computationally efficient algorithm for this optimization problem.

Main Methods:

• Formulation of the optimal attack energy allocation as a Markov decision process (MDP).
• Development of a backward induction algorithm based on MDP to find the optimal strategy.
• Analysis of signal-to-noise ratio (SINR) in the context of remote state estimation under attack.

Main Results:

• The proposed backward induction algorithm effectively determines the optimal eavesdropper attack energy allocation.
• The algorithm achieves lower computational cost compared to traditional induction methods.
• Numerical simulations validate the theoretical analysis and the effectiveness of the proposed strategy.

Conclusions:

• The MDP-based backward induction algorithm provides an efficient solution for optimizing eavesdropper energy allocation in CPS.
• This research contributes to enhancing the security of remote state estimation in CPS against sophisticated eavesdropping.
• The findings are crucial for designing more robust and secure cyber-physical systems.