Synthesizing Behavioural Models of CPS Using Automata Learning and Statistical Machine Learning

2026-08-08

AI Summary

This work addresses the challenge of directly applying numerical time-series trajectories from cyber-physical systems (CPS) to formal verification by proposing MELA, a novel method that systematically integrates information-theoretic variable selection with decision tree–based interval abstraction to achieve fully automated, unsupervised numerical-to-symbolic conversion. By coupling this transformation with passive automata learning, MELA synthesizes compact, interpretable behavioral models from raw signals that exhibit strong correlation with underlying system states. Evaluated on two CPS case studies, MELA reduces the number of states and transitions by 49.20% on average while improving model accuracy by 41.71%, thereby effectively enabling system-level requirement verification and uncovering implicit system behaviors.

Abstract

Inferring behavioural models from system executions is essential for supporting formal verification and analysis of complex, heterogeneous cyber-physical systems (CPS). Automata learning provides an effective way to infer state machine models from system executions. However, CPS inputs and outputs often consist of numeric time-series data, while automata learning algorithms assume inputs over a finite symbolic alphabet. As a result, raw numeric data must first be abstracted into a finite set of symbols. In this article, we present MELA, a passive automata learning approach enhanced with machine learning to synthesize behavioural models from numeric time-series data generated by CPS. MELA systematically combines statistical machine learning with automata learning to automatically abstract raw numeric signals into interpretable intervals that are strongly correlated with system states. Specifically, MELA uses information-theoretic variable selection and decision-tree-based range abstraction to transform numeric traces into symbolic representations suitable for automata learning. We evaluate MELA on two CPS: a commercial network intrusion detection system developed by our industry partner, RabbitRun Technologies, and a publicly available industrial autopilot benchmark from the aerospace domain. Compared with expertise-based numeric data abstraction, MELA reduces the number of states and transitions in the learned state machines by 49.20% on average, while improving accuracy by 41.71% on average. Furthermore, the learned state machines support system-level requirement verification and help practitioners explore behaviours that are not explicit in the system requirements. We make our implementation and experimental data available online. Keywords: Automata learning, Cyber-physical systems, Behavioural model synthesis, Decision trees, Model checking, Intrusion detection, Simulink.

Problem

• Cyber-physical systems
• Behavioural model synthesis
• Automata learning
• Time-series data abstraction
• Formal verification

Innovation

• Automata learning
• Statistical machine learning
• Decision trees
• Behavioural model synthesis
• Cyber-physical systems