Sparse Actuator Attack Detection and Identification: A Data-Driven Approach.

Area of Science:

• Cyber-Physical Systems Security
• Control Theory
• Signal Processing

Background:

• Cyber-physical systems (CPS) are increasingly vulnerable to sophisticated attacks.
• Sparse actuator attacks pose a significant threat to CPS integrity and safety.
• Existing detection methods may not adequately address sparse attack scenarios.

Purpose of the Study:

• To develop data-driven methods for detecting and identifying sparse actuator attacks in CPS.
• To formulate robust attack models and design effective detection and identification algorithms.
• To analyze the security and vulnerability of CPS under these attack models.

Main Methods:

• Formulation of sparse additive and multiplicative actuator attack models.
• Development of a data-driven attack detector using stable kernel representation.
• Application of subspace identification and compressive sensing theories.
• Proposal of sparse recovery-based attack identification policies using convex optimization.

Main Results:

• Successful design of an attack detector for cyber-physical systems.
• Development of two novel sparse recovery-based attack identification policies.
• Analysis of identifiability conditions to assess system vulnerability.
• Validation of proposed methods through simulations on a flight vehicle system.

Conclusions:

• The proposed data-driven methods effectively detect and identify sparse actuator attacks in CPS.
• Subspace identification and compressive sensing offer powerful tools for enhancing CPS security.
• The developed techniques provide a framework for evaluating CPS vulnerability to sparse attacks.