AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System
This addresses the problem of costly and inefficient attack data collection for ICS security, offering a scalable solution for researchers and practitioners, though it is incremental as it builds on existing LLM and multi-agent methods.
The paper tackles the challenge of generating malicious attack patterns for evaluating machine learning robustness in Industrial Control Systems (ICS) by proposing a novel approach that uses large language models (LLMs) to create these patterns, resulting in patterns that surpass human experts in quality and quantity.
Malicious examples are crucial for evaluating the robustness of machine learning algorithms under attack, particularly in Industrial Control Systems (ICS). However, collecting normal and attack data in ICS environments is challenging due to the scarcity of testbeds and the high cost of human expertise. Existing datasets are often limited by the domain expertise of practitioners, making the process costly and inefficient. The lack of comprehensive attack pattern data poses a significant problem for developing robust anomaly detection methods. In this paper, we propose a novel approach that combines data-centric and design-centric methodologies to generate attack patterns using large language models (LLMs). Our results demonstrate that the attack patterns generated by LLMs not only surpass the quality and quantity of those created by human experts but also offer a scalable solution that does not rely on expensive testbeds or pre-existing attack examples. This multi-agent based approach presents a promising avenue for enhancing the security and resilience of ICS environments.