A Passive Online Technique for Learning Hybrid Automata from Input/Output Traces

TL;DR

This paper introduces a passive, online method for synthesizing hybrid automata from input-output traces of non-linear cyber-physical systems, using Dynamic Time Warping to handle non-linear behavior similarity.

Contribution

It presents a novel passive and online approach for hybrid automata synthesis that does not require system interaction and improves with each new trace using similarity detection.

Findings

Promising accuracy in industrial and simulated case studies

Effective handling of non-linear behavior with Dynamic Time Warping

Automata improve iteratively with new input-output traces

Abstract

Specification synthesis is the process of deriving a model from the input-output traces of a system. It is used extensively in test design, reverse engineering, and system identification. One type of the resulting artifact of this process for cyber-physical systems is hybrid automata. They are intuitive, precise, tool independent, and at a high level of abstraction, and can model systems with both discrete and continuous variables. In this paper, we propose a new technique for synthesizing hybrid automaton from the input-output traces of a non-linear cyber-physical system. Similarity detection in non-linear behaviors is the main challenge for extracting such models. We address this problem by utilizing the Dynamic Time Warping technique. Our approach is passive, meaning that it does not need interaction with the system during automata synthesis from the logged traces; and online, which means that each input/output trace is used only once in the procedure. In other words, each new trace can be used to improve the already synthesized automaton. We evaluated our algorithm in two industrial and simulated case studies. The accuracy of the derived automata show promising results.