  With the advancement of IoT technology, many electronic devices are
interconnected through networks, communicating with each other and performing
specific roles. However, as numerous devices join networks, the threat of
cyberattacks also escalates. Preventing and detecting cyber threats are
crucial, and one method of preventing such threats involves using attack
graphs. Attack graphs are widely used to assess security threats within
networks. However, a drawback emerges as the network scales, as generating
attack graphs becomes time-consuming. To overcome this limitation, artificial
intelligence models can be employed. By utilizing AI models, attack graphs can
be created within a short period, approximating optimal outcomes. AI models
designed for attack graph generation consist of encoders and decoders, trained
using reinforcement learning algorithms. After training the AI models, we
confirmed the model's learning effectiveness by observing changes in loss and
reward values. Additionally, we compared attack graphs generated by the AI
model with those created through conventional methods.
