Division of labor in CPS anomaly detection
Balancing models, LLMs, data scientists, and users
Publication date
2025-05-27
Document type
Konferenzbeitrag
Organisational unit
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2025
First page
34
Last page
47
Part of the university bibliography
✅
Language
English
Keyword
Self-supervised learning
Large language model
Anomaly detection
Abstract
Anomaly detection in multivariate time series is critical for ensuring the reliability of cyber-physical systems (CPS). We propose a two-stage framework that combines advanced anomaly detection models with large language models (LLMs) to provide robust detection and interpretable explanations. In the first stage, a self-supervised ensemble of temporal and spatiotemporal models identifies anomalies based on reconstruction errors. In the second stage, LLMs generate natural language explanations for these anomalies, making results accessible to domain experts.
To address LLM limitations such as hallucination and instruction adherence, we design structured prompts that provide focused context, anomaly details, and clear guidelines. This framework emphasizes a division of labor between detection models, LLMs, data scientists, and users. We validate the approach using data from a search-and-rescue cruiser, showcasing its ability to detect diverse anomalies and provide interpretable outputs. This work bridges advanced machine learning with practical CPS applications, offering a path towards a user-friendly approach to anomaly detection.
To address LLM limitations such as hallucination and instruction adherence, we design structured prompts that provide focused context, anomaly details, and clear guidelines. This framework emphasizes a division of labor between detection models, LLMs, data scientists, and users. We validate the approach using data from a search-and-rescue cruiser, showcasing its ability to detect diverse anomalies and provide interpretable outputs. This work bridges advanced machine learning with practical CPS applications, offering a path towards a user-friendly approach to anomaly detection.
Version
Published version
Access right on openHSU
Open access