  We propose a new method to quantify the impact of cyber attacks in Cyber
Physical Systems (CPSs). In particular, our method allows to identify the
Design Parameter (DPs) affected due to a cyber attack launched on a different
set of DPs in the same CPS. To achieve this, we adopt causal graphs to causally
link DPs with each other and quantify the impact of one DP on another. Using
SWaT, a real world testbed of a water treatment system, we demonstrate that
causal graphs can be build in two ways: i) using domain knowledge of the
control logic and the physical connectivity structure of the DPs, we call these
causal domain graphs and ii) learning from operational data logs, we call these
causal learnt graphs. We then compare these graphs when a same set of DPs is
used. Our analysis shows a common set of edges between the causal domain graphs
and the causal learnt graphs exists, which helps validate the causal learnt
graphs. Additionally, we show that the learnt graphs can discover new causal
relations, not initially considered in the domain graphs, that help
significantly characterising the impact of the attack. We use causal domain
graphs to estimate the parameters of the graphs, and the causal learnt graphs
for causal inference. To learn the structure of the causal learnt graphs in all
the six-stages of SWaT, we experiment with three learning algorithms: Peter
Clarke (PC), Hill Climb (HC) search and Chow-Lie (CH). Finally, we demonstrate
how causal graphs can be used to analyse the impact of cyber attacks by
analysing nine well known cyber attacks on the SWaT test bed. We find that by
using causal learnt graphs the DPs impacted by the attacks are correctly
discovered with a probability greater than 0.9.
