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.

翻译：随着物联网技术的发展，许多电子设备通过网络相互连接，彼此通信并执行特定功能。然而，随着大量设备接入网络，网络攻击的威胁也随之增加。预防和检测网络威胁至关重要，而预防此类威胁的方法之一是利用攻击图。攻击图被广泛用于评估网络中的安全威胁。然而，随着网络规模的扩大，一个缺点逐渐显现：生成攻击图变得耗时。为克服这一限制，可以采用人工智能模型。通过利用AI模型，可以在较短时间内生成攻击图，并接近最优结果。专为攻击图生成设计的AI模型由编码器和解码器组成，通过强化学习算法进行训练。在训练AI模型后，我们通过观察损失值和奖励值的变化来确认模型的学习效果。此外，我们将AI模型生成的攻击图与传统方法生成的攻击图进行了比较。