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Optimal Security Protection Strategy Selection Model Based on Q-Learning Particle Swarm Optimization.

Xin Gao1, Yang Zhou1, Lijuan Xu1

  • 1Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.

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|December 23, 2022
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Summary
This summary is machine-generated.

This study presents an optimal security protection strategy for industrial control systems (ICS) using a Q-Learning particle swarm optimization (QLPSO) model. The method effectively balances security risks and protection costs for enhanced industrial cybersecurity.

Keywords:
Bayesian attack graphQ-Learningoptimal protection strategyparticle swarm optimization

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Area of Science:

  • Cybersecurity
  • Industrial Control Systems (ICS)
  • Optimization Algorithms

Background:

  • Industrial Internet of Things (IIoT) expansion increases security threats to ICS.
  • Developing cost-effective and robust protection strategies for ICS is crucial.
  • Optimizing security strategies considering both risk and cost presents significant challenges.

Purpose of the Study:

  • To propose an optimal security protection strategy selection model for ICS.
  • To develop an efficient optimization framework for selecting these strategies.
  • To address the challenge of balancing security risks and protection costs in ICS.

Main Methods:

  • Introduced a Bayesian attack graph incorporating protection strategies for ICS security risk assessment.
  • Developed a Q-Learning particle swarm optimization (QLPSO) framework.
  • Utilized Q-Learning to enhance the Particle Swarm Optimization (PSO) algorithm's performance.

Main Results:

  • The proposed model and QLPSO algorithm were simulated on a water distribution ICS.
  • Simulation results demonstrated the validity and feasibility of the developed approach.
  • The QLPSO algorithm showed improvements in addressing local optima, diversity, and precision issues in PSO.

Conclusions:

  • The study successfully developed and validated an optimal security protection strategy selection model for ICS.
  • The QLPSO framework provides an effective method for optimizing security investments in industrial environments.
  • The findings contribute to enhancing the security and resilience of critical industrial infrastructure.