ICCPS: Impact discovery using causal inference for cyber attacks in CPSs (2307.14161v1)

Abstract: 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.