AI-based Attack Graph Generation

• URL: http://arxiv.org/abs/2311.14342v2
• Date: Mon, 27 Nov 2023 07:22:24 GMT
• Title: AI-based Attack Graph Generation
• Authors: Sangbeom Park, Jaesung Lee, Jeong Do Yoo, Min Geun Song, Hyosun Lee, Jaewoong Choi, Chaeyeon Sagong, Huy Kang Kim,
• Abstract summary: Attack graphs are widely used to assess security threats within networks. A drawback emerges as the network scales, as generating attack graphs becomes time-consuming. By utilizing AI models, attack graphs can be created within a short period, approximating optimal outcomes.
• Score: 7.282532608209566
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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