The application discloses a black-box
home automation rule set
reconstruction method based on interactive
verification and belongs to the technical field of
network security. In view of the deficiencies existing in the current intelligent
home automation rule discovery and management, the confidence between the trigger attribute and the condition attribute is established through
knowledge learning, the efficiency of obtaining rules is improved, and the inefficiency of exhaustion is effectively overcome. The application also proposes a rule causal chain modeling method based on
event sequence analysis. By analyzing the
time sequence of entity state interaction, different entity state changes occurring continuously are mapped into a "trigger entity-action entity" pair, and entities maintaining a
stable state between the trigger and the action are automatically identified as condition candidate entities, so that the automatic generation and structured expression of the
automation rule are realized. Experimental results show that the application achieves good results in collecting
automation rules in the real world. Therefore, the application can be used for intelligent home
system anomaly detection and network space security maintenance.