LLMPot: Automated LLM-based Industrial Protocol and Physical Process Emulation for ICS Honeypots
<|reference_start|>LLMPot: Automated LLM-based Industrial Protocol and Physical Process Emulation for ICS Honeypots: Industrial Control Systems (ICS) are extensively used in critical infrastructures ensuring efficient, reliable, and continuous operations. However, their increasing connectivity and addition of advanced features make them vulnerable to cyber threats, potentially leading to severe disruptions in essential services. In this context, honeypots play a vital role by acting as decoy targets within ICS networks, or on the Internet, helping to detect, log, analyze, and develop mitigations for ICS-specific cyber threats. Deploying ICS honeypots, however, is challenging due to the necessity of accurately replicating industrial protocols and device characteristics, a crucial requirement for effectively mimicking the unique operational behavior of different industrial systems. Moreover, this challenge is compounded by the significant manual effort required in also mimicking the control logic the PLC would execute, in order to capture attacker traffic aiming to disrupt critical infrastructure operations. In this paper, we propose LLMPot, a novel approach for designing honeypots in ICS networks harnessing the potency of Large Language Models (LLMs). LLMPot aims to automate and optimize the creation of realistic honeypots with vendor-agnostic configurations, and for any control logic, aiming to eliminate the manual effort and specialized knowledge traditionally required in this domain. We conducted extensive experiments focusing on a wide array of parameters, demonstrating that our LLM-based approach can effectively create honeypot devices implementing different industrial protocols and diverse control logic.<|reference_end|>
@article{vasilatos2024llmpot:,
    title={LLMPot: Automated LLM-based Industrial Protocol and Physical Process
  Emulation for ICS Honeypots},
    author={Christoforos Vasilatos, Dunia J. Mahboobeh, Hithem Lamri, Manaar Alam
  and Michail Maniatakos},
    journal={arXiv preprint arXiv:2405.05999},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05999},
    primaryClass={cs.CR cs.LG}
}