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，众多电子设备通过网络互联，相互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，其中一种方法便是利用攻击图。攻击图被广泛应用于评估网络中的安全威胁。然而，随着网络规模的扩大，生成攻击图变得耗时费力。为克服这一局限性，可以引入人工智能模型。通过利用AI模型，可在短时间内生成近似最优结果的攻击图。专为攻击图生成设计的AI模型包含编码器和解码器，并使用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确认了模型的学习效果。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。