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Injection attack estimation of networked control systems subject to hidden DoS attack.

Jie Shao1, Zehua Ye1, Dan Zhang1

  • 1Research Center of Automation and Artificial Intelligence, Zhejiang University of Technology, Hangzhou, 310023, China.

ISA Transactions
|February 26, 2022
PubMed
Summary

This study develops an estimator for Deny-of-Service (DoS) attacks in networked control systems, effectively estimating injected signals under stochastic hidden DoS attacks. The method ensures estimation error boundedness, demonstrating practical effectiveness through simulations and experiments.

Keywords:
-error mean square stabilityDoS attackInjection attackIntermediate estimationSemi-Markov system

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Area of Science:

  • Control Systems Engineering
  • Network Security
  • Stochastic Systems Analysis

Background:

  • Networked control systems are vulnerable to Deny-of-Service (DoS) attacks, which inject unknown signals into sensors and actuators.
  • Estimating these injected signals under stochastic hidden DoS attacks is crucial for system security and stability.
  • Existing methods may not fully capture the complex, aperiodic sampling behavior induced by such attacks.

Purpose of the Study:

  • To design an effective estimator for injection attacks in networked control systems experiencing stochastic hidden DoS attacks.
  • To model the aperiodic sampling behavior caused by DoS attacks using a semi-Markov chain.
  • To analyze the stability and boundedness of the estimation error system.

Main Methods:

  • Modeling DoS attack-induced aperiodic sampling as a stochastic switching system using a semi-Markov chain.
  • Applying Lyapunov stability theory and stochastic system analysis to derive stability conditions.
  • Utilizing linear matrix inequalities to determine optimal estimation gain matrices.

Main Results:

  • The σ-error mean square stability (σ-EMSS) conditions for the estimation error system were derived.
  • The state of the attack estimation error system was proven to be uniformly ultimately bounded.
  • Simulation and experimental studies on a motor system validated the proposed estimation method's effectiveness.

Conclusions:

  • The developed estimator effectively addresses injection attacks in networked control systems under stochastic hidden DoS conditions.
  • The semi-Markov chain modeling accurately captures the complex dynamics introduced by DoS attacks.
  • The results provide a robust framework for securing networked control systems against sophisticated cyber threats.