1. Determining and Sharing Risk Data in Distributed Interdependent Systems
- Author
-
Burnap, Peter, Cherdantseva, Yulia, Blyth, Andrew, Eden, Peter, Jones, Kevin, Soulsby, Hugh, Stoddart, Kristan, and Airbus Group Innovations
- Subjects
QA75 ,Risk analysis ,021110 strategic, defence & security studies ,General Computer Science ,business.industry ,Computer science ,media_common.quotation_subject ,0211 other engineering and technologies ,Complex system ,02 engineering and technology ,Computer security ,computer.software_genre ,Data modeling ,Interdependence ,Risk analysis (engineering) ,Vulnerability assessment ,Risk analysis (business) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,business ,Risk assessment ,Failure mode and effects analysis ,computer ,Risk management ,media_common - Abstract
While the risks induced by system dependencies have been studied; little is known about modelling complex collections of supposedly independent systems at different geographical locations, which are in reality interdependent due to sharing often-unrecognized common elements. It could be argued that any risk analysis of a large infrastructure that does not take account of such interdependencies is dangerously introspective. We present a top-down, goal-to-dependencies approach to modelling and understanding such Complex Systems, which uses secure, distributed computing protocols to share risk data between the risk models of interdependent systems. We present a Bayesian-sensitivity measure of risk, which is both intuitively satisfying and accords with everyday notions of risk. The core benefit of this approach is to capture dependencies between systems and share risk data such that failure of an entity along the ‘supply chain’ can be rapidly propagated to those who depend on it allowing them to calculate the likely impact and respond accordingly.
- Published
- 2017