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Double reinforcement learning for cluster synchronization of Boolean control networks under denial of service attacks

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  • Wanqiu Deng
  • Chi Huang
  • Qinghong Shuai

Abstract

This paper investigates the asymptotic cluster synchronization of Boolean control networks (BCNs) under denial-of-service (DoS) attacks, where each state node in the network experiences random data loss following a Bernoulli distribution. First, the algebraic representation of BCNs under DoS attacks is established using the semi-tensor product (STP) of matrices. Using matrix-based methods, some necessary and sufficient algebraic conditions for BCNs to achieve asymptotic cluster synchronization under DoS attacks are derived. For both model-based and model-free cases, appropriate state feedback controllers guaranteeing asymptotic cluster synchronization of BCNs are obtained through set-iteration and double-deep Q-network (DDQN) methods, respectively. Besides, a double reinforcement learning algorithm is designed to identify suitable state feedback controllers. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • Wanqiu Deng & Chi Huang & Qinghong Shuai, 2025. "Double reinforcement learning for cluster synchronization of Boolean control networks under denial of service attacks," PLOS ONE, Public Library of Science, vol. 20(7), pages 1-21, July.
    • Handle: RePEc:plo:pone00:0327252
    DOI: 10.1371/journal.pone.0327252
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