Behavior learning system, behavior learning method and program
An action learning system includes a memory, and a processor configured to train, based on first data indicating a property of an environment in which data is collected from multiple devices and to which an action determined by a first neural network according to a state of the environment is applied and second data indicating a property of the environment to which the action is not applied, a second neural network that calculates a similarity degree between distributions of the first data and the second data, and train, after the second neural network is trained, the first neural network that determines an action according to the state of the environment, by reinforcement learning including, in a reward, a value that changes based on a relationship between a similarity degree and a parameter set by a user, the similarity degree being calculated by the second neural network based.
1. An action learning system comprising:
a memory; and
a processor configured to train, based on first data indicating a property of an environment in which data is collected from a plurality of devices and to which an action determined by a first neural network according to a state of the environment is applied and second data indicating a property of the environment to which the action is not applied, a second neural network,
train, after the second neural network is trained, the first neural network that determines an action according to the state of the environment, by reinforcement learning using a reward including a value that changes based on a relationship between a similarity degree and a parameter set by a user, the similarity degree being calculated by the second neural network based on third data indicating a property of the environment to which an action determined by the first neural network is applied, wherein the reward is a value that increases as the similarity degree is closer to the parameter set by the user; and
generating, by the trained first neural network, an example network attack action that causes a cyberattack to a network to suppress execution of the cyberattack according to the example network attack action, wherein the example network attack action, if executed, causes a damage to the network while maintaining abnormality in the network caused by the cyberattack undetected based on the similarity degree.
2. The action learning system according to claim 1 , wherein
the reward is a value that increases as the similarity degree is closer to the parameter set by the user.
3. The action learning system according to claim 2 , wherein
the processor trains the first neural network by reinforcement learning using the reward including a value of a Lorenz function that uses the parameter as a location parameter and the similarity degree as a variable.
4. The action learning system according to claim 1 , wherein
the processor trains the second neural network according to a GAN (generative adversarial network) algorithm.
5. An action learning method for execution by a computer, the action learning method comprising:
training, based on first data indicating a property of an environment in which data is collected from a plurality of devices and to which an action determined by a first neural network according to a state of the environment is applied and second data indicating a property of the environment to which the action is not applied, a second neural network; and
training, after the second neural network is trained, the first neural network that determines an action according to the state of the environment, by reinforcement learning using a reward including a value that changes based on a relationship between a similarity degree is to a parameter set by a user, the similarity degree being calculated by the second neural network based on third data indicating a property of the environment to which an action determined by the first neural network is applied,
wherein the reward is a value that increases as the similarity degree is closer to the parameter set by the user; and
generating, by the trained first neural network, an example network attack action that causes a cyberattack to a network to suppress execution of the cyberattack according to the example network attack action, wherein the example network attack action, if executed, causes a damage to the network while maintaining abnormality in the network caused by the cyberattack undetected based on the similarity degree.
6. A non-transitory computer-readable recording medium having stored therein a program for causing a computer to execute a process comprising:
training, based on first data indicating a property of an environment in which data is collected from a plurality of devices and to which an action determined by a first neural network according to a state of the environment is applied and second data indicating a property of the environment to which the action is not applied, a second neural network; and
training, after the second neural network is trained, the first neural network that determines an action according to the state of the environment, by reinforcement learning using a reward including a value that changes based on a relationship between a similarity degree is to a parameter set by a user, the similarity degree being calculated by the second neural network based on third data indicating a property of the environment to which an action determined by the first neural network is applied,
wherein the reward is a value that increases as the similarity degree is closer to the parameter set by the user; and
generating, by the trained first neural network, an example network attack action that causes a cyberattack to a network to suppress execution of the cyberattack according to the example network attack action, wherein the example network attack action, if executed, causes a damage to the network while maintaining abnormality in the network caused by the cyberattack undetected based on the similarity degree.