Extending FAST-CPS for the analysis of data flows in cyber-physical systems

Abstract

Cyber-physical systems are increasingly automated and interconnected. Strategies like predictive maintenance are on the rise and as a result new streams of data will flow through these systems. This data is often confidential, which can be a problem in these low-security systems. In addition, more stakeholders are now involved and various cloud-based service providers are utilised. Companies often no longer know who gets to see their data. This paper presents a methodology that aims to analyse these data flows. The methodology takes as input a set of data asset preferences and service policies, as well as the asset flow of the system. It then returns feedback in the form of an asset profile showing which stakeholders have access to what data assets, and conflicts between the preferences and the modeled situation. Several possible actors with different preferences are modeled for each stakeholder role in the system, the scenarios with the fewest conflicts are returned. The methodology is validated on a case study and has been added to the FAST-CPS framework.