Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation

TL;DR

This paper presents a novel approach using a Large Language Model to interpret BACnet protocol packets, providing clear summaries to assist SOC analysts in understanding complex ICS network communications.

Contribution

It introduces a LLM-based software solution for BACnet packet interpretation, integrating context retrieval and summarization to improve ICS security monitoring.

Findings

Enhanced packet understanding for SOC analysts

Effective summarization of complex network data

Improved security incident response capabilities

Abstract

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.