INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection

• URL: http://arxiv.org/abs/2411.10918v2
• Date: Mon, 02 Jun 2025 16:27:40 GMT
• Title: INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection
• Authors: Danial Abshari, Peiran Shi, Chenglong Fu, Meera Sridhar, Xiaojiang Du,
• Abstract summary: Cyber-Physical Systems (CPS) are vulnerable to cyber-physical attacks that violate physical laws.<n>We propose a hybrid framework that uses large language models (LLMs) to extract semantic information from CPS documentation and generate physical invariants.<n>This approach combines LLM semantic understanding with empirical validation to ensure both interpretability and reliability.
• Score: 13.192308838452927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cyber-Physical Systems (CPS) are vulnerable to cyber-physical attacks that violate physical laws. While invariant-based anomaly detection is effective, existing methods are limited: data-driven approaches lack semantic context, and physics-based models require extensive manual work. We propose INVARLLM, a hybrid framework that uses large language models (LLMs) to extract semantic information from CPS documentation and generate physical invariants, then validates these against real system data using a PCMCI+-inspired K-means method. This approach combines LLM semantic understanding with empirical validation to ensure both interpretability and reliability. We evaluate INVARLLM on SWaT and WADI datasets, achieving 100% precision in anomaly detection with no false alarms, outperforming all existing methods. Our results demonstrate that integrating LLM-derived semantics with statistical validation provides a scalable and dependable solution for CPS security.

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