The invention discloses an information
physical system security control method based on deep
reinforcement learning, and belongs to the technical field of
information security. According to the invention, the problem of
poor control performance of a security control strategy designed based on an existing method under the condition of
network attack is solved. According to the method, the
dynamic equation of the cyber-
physical system under the attacked condition is described as a Markov
decision process, and based on the established Markov process, the security control problem of the cyber-
physical system under the false data injection
attack condition is converted into a control strategy
learning problem only using data; based on a flexible action-critic
reinforcement learning algorithm framework, a flexible action-critic
reinforcement learning algorithm based on a
Lyapunov function is proposed, a novel deep neural network training framework is provided, a
Lyapunov stability theory is fused in the
design process, the stability of an information physical
system is ensured, and the control performance is effectively improved. The method can be applied to
safety control of the information physical
system.