False Data Injection Attack for Cyber-Physical Systems With Resource Constraint.

Area of Science:

• Cyber-physical Systems Security
• Control Theory
• Optimization

Background:

• Cyber-physical systems (CPSs) are critical infrastructure vulnerable to cyber-attacks.
• Understanding attacker behavior is essential for developing effective defensive measures.
• Resource constraints on attackers necessitate strategic sensor targeting.

Purpose of the Study:

• To investigate false data injection attacks on remote state estimation in CPSs.
• To analyze attacker strategies under resource limitations, specifically sensor selection.
• To develop an optimal attack strategy maximizing estimation error covariance.

Main Methods:

• Formulating the attack problem considering two Gaussian noise injection scenarios.
• Applying matrix theory to transform the attack strategy problem.
• Converting the problem into a solvable convex optimization problem.

Main Results:

• An optimal sensor attack strategy is derived to maximize the trace of the remote estimation error covariance.
• The study provides a method to determine which sensors an attacker should target.
• Theoretical results are validated through a practical example.

Conclusions:

• The developed optimal attack strategy effectively maximizes estimation errors under resource constraints.
• This research contributes to understanding and mitigating cyber-attacks on CPSs.
• The findings aid in designing more robust cyber-security defenses for CPSs.