Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation
<|reference_start|>Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation: The Industrial Control System (ICS) environment encompasses a wide range of intricate communication protocols, posing substantial challenges for Security Operations Center (SOC) analysts tasked with monitoring, interpreting, and addressing network activities and security incidents. Conventional monitoring tools and techniques often struggle to provide a clear understanding of the nature and intent of ICS-specific communications. To enhance comprehension, we propose a software solution powered by a Large Language Model (LLM). This solution currently focused on BACnet protocol, processes a packet file data and extracts context by using a mapping database, and contemporary context retrieval methods for Retrieval Augmented Generation (RAG). The processed packet information, combined with the extracted context, serves as input to the LLM, which generates a concise packet file summary for the user. The software delivers a clear, coherent, and easily understandable summary of network activities, enabling SOC analysts to better assess the current state of the control system.<|reference_end|>
@article{sharma2024decoding,
    title={Decoding BACnet Packets: A Large Language Model Approach for Packet
  Interpretation},
    author={Rashi Sharma, Hiroyuki Okada, Tatsumi Oba, Karthikk Subramanian, Naoto
  Yanai, Sugiri Pranata},
    journal={arXiv preprint arXiv:2407.15428},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15428},
    primaryClass={cs.CR cs.AI}
}