Q-learning Based Optimal False Data Injection Attack on Probabilistic
  Boolean Control Networks
Xianlun Peng Yang Tang Fangfei Li Yang Liu
In this paper, we present a reinforcement learning (RL) method for solving
optimal false data injection attack problems in probabilistic Boolean control
networks (PBCNs) where the attacker lacks knowledge of the system model.
Specifically, we employ a Q-learning (QL) algorithm to address this problem. We
then propose an improved QL algorithm that not only enhances learning
efficiency but also obtains optimal attack strategies for large-scale PBCNs
that the standard QL algorithm cannot handle. Finally, we verify the
effectiveness of our proposed approach by considering two attacked PBCNs,
including a 10-node network and a 28-node network.