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Security Analysis of Cyber-Physical Systems Using Reinforcement Learning.

Mariam Ibrahim1, Ruba Elhafiz1

  • 1Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan.

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Summary
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This study uses reinforcement learning (RL) to find the worst-case cyber-attack scenarios in smart grids, identifying critical system weaknesses for improved cyber security.

Keywords:
SARSAcyber securityoptimal pathreinforcement learning

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Area of Science:

  • Cyber-Physical Systems (CPSs)
  • Artificial Intelligence
  • Smart Grid Security

Background:

  • Future engineering systems rely on advanced Cyber-Physical Systems (CPSs) for enhanced autonomy and security.
  • Assessing the security vulnerabilities of CPSs, particularly in critical infrastructure like smart grids, is crucial.

Purpose of the Study:

  • To investigate the security of Cyber-Physical Systems (CPSs) using a smart grid as a case study.
  • To identify critical subsystems' weaknesses by employing a reinforcement learning (RL) augmented attack graph.

Main Methods:

  • Utilized the State Action Reward State Action (SARSA) reinforcement learning technique, with the agent acting as an attacker.
  • Developed an attack graph representing the smart grid environment for the SARSA agent.
  • Employed rewards and penalties within SARSA to determine the most damaging attack path.

Main Results:

  • Successfully identified the worst-case attack scenario within the smart grid system.
  • The worst-case scenario yielded a total reward of 26.9.
  • Pinpointed the specific subsystems most vulnerable to severe damage.

Conclusions:

  • The SARSA RL-augmented attack graph is effective in uncovering critical security vulnerabilities in smart grids.
  • This method aids in understanding attacker strategies and prioritizing defenses for enhanced CPS security.