Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2016

Abstract

Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. We present a preliminary study on a water treatment system that suggests the efficacy of this approach, propose strategies for establishing confidence in the correctness of invariants, then summarise some research questions and the steps we are taking to investigate them.

Keywords

Support Vector Machine, Sensor Data, Water Treatment Plant, Mutation Testing, Programmable Logic Controller

Discipline

Software Engineering | Theory and Algorithms

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 21st International Symposium Limassol, Cyprus, 2016 November 9–11

Volume

9995

First Page

155

Last Page

163

ISBN

9783319489889

Identifier

10.1007/978-3-319-48989-6_10

Publisher

Springer Link

City or Country

Cyprus

Additional URL

https://doi.org/10.1007/978-3-319-48989-6_10

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