Automated honeypot creation within a network

A method, comprising:

• receiving a call from a client device;
• classifying the call as unauthorized based on call data associated with the call;
• obtaining, from a routing model, routing data for the call based on the call data associated with the call;
• and
• sending, based on the classifying and the routing data, the call to a node-imitating model of a plurality of node-imitating models such that (i) the call is sent to a first node-imitating model in response to the call data being first call data and (ii) the call is sent to a second node-imitating model in response to the call data being other call data different from the first call data,
• wherein (i) the first node-imitating model is trained based on a first set of training data related to a first set of prior calls that is provided to the first node-imitating model and (ii) the second node-imitating model is trained based on a second set of training data related to a second set of prior calls that is provided to the second node-imitating model, and
• wherein the first and second node-imitating models have different configurations as a result of the training of the first and second node-imitating models.

A non-transitory computer readable medium comprising instructions that, when executed by one or more processors, perform operations comprising:

• receiving a call from a client device;
• classifying the call as unauthorized based on call data associated with the call;
• obtaining, from a routing model, routing data for the call based on the call data associated with the call;
• and
• sending, based on the classifying and the routing data, the call to a node-imitating model of a plurality of node-imitating models such that (i) the call is sent to a first node-imitating model in response to the call data being first call data and (ii) the call is sent to a second node-imitating model in response to the call data being other call data different from the first call data,
• wherein (i) the first node-imitating model is trained based on a first set of training data related to a first set of prior calls that is provided to the first node-imitating model and (ii) the second node-imitating model is trained based on a second set of training data related to a second set of prior calls that is provided to the second node-imitating model, and
• wherein the first and second node-imitating models have different configurations as a result of the training of the first and second node-imitating models.