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Reinforcement learning-based optimal false data injection attack against control signals in probabilistic Boolean control networks

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  • Peng, Xianlun
  • Li, Fangfei
  • Liu, Yang

Abstract

In this paper, we focus on the challenges of optimal false-data injection attacks in Probabilistic Boolean Control Networks (PBCNs) where attackers lack system model knowledge. Attack optimization is formulated as a Markov Decision Process (MDP) policy search problem. Theoretical guidance for reward design in the MDP is provided, proving the existence of optimal policies under Theorem 1. An enhanced Q-learning (QL) algorithm is developed to overcome scalability limitations of the standard QL in large-scale PBCNs. Experimental validation on both 10-node and 28-node networks demonstrates the efficacy of the method.

Suggested Citation

  • Peng, Xianlun & Li, Fangfei & Liu, Yang, 2026. "Reinforcement learning-based optimal false data injection attack against control signals in probabilistic Boolean control networks," Applied Mathematics and Computation, Elsevier, vol. 519(C).
    • Handle: RePEc:eee:apmaco:v:519:y:2026:i:c:s0096300325006642
    DOI: 10.1016/j.amc.2025.129939
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    References listed on IDEAS

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    1. Ji, Hankang & Li, Yuanyuan & Ding, Xueying & Alghamdi, Sultan M. & Lu, Jianquan, 2024. "Stability analysis of Boolean networks: An eigenvalue approach," Applied Mathematics and Computation, Elsevier, vol. 463(C).
    2. Pedro J. Rivera Torres & E. I. Serrano Mercado & Luis Anido Rifón, 2018. "Probabilistic Boolean network modeling of an industrial machine," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 875-890, April.
    3. Xiao Li & Hanchen Xu & Jinming Zhang & Hua-hua Chang, 2023. "Deep Reinforcement Learning for Adaptive Learning Systems," Journal of Educational and Behavioral Statistics, , vol. 48(2), pages 220-243, April.
    4. Wen Wang & Beibei Li & Xueming Luo & Xiaoyi Wang, 2023. "Deep Reinforcement Learning for Sequential Targeting," Management Science, INFORMS, vol. 69(9), pages 5439-5460, September.
    5. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Luis Anido Rifón, 2018. "Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1393-1413, August.
    6. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    7. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Luis Anido Rifón, 2018. "Erratum to: Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1415-1415, August.
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