  Recent years have seen increased interest in the design and deployment of
smart grid devices and control algorithms. Each of these smart communicating
devices represents a potential access point for an intruder spurring research
into intruder prevention and detection. However, no security measures are
complete, and intruding attackers will compromise smart grid devices leading to
the attacker and the system operator interacting via the grid and its control
systems. The outcome of these machine-mediated human-human interactions will
depend on the design of the physical and control systems mediating the
interactions. If these outcomes can be predicted via simulation, they can be
used as a tool for designing attack-resilient grids and control systems.
However, accurate predictions require good models of not just the physical and
control systems, but also of the human decision making. In this manuscript, we
present an approach to develop such tools, i.e. models of the decisions of the
cyber-physical intruder who is attacking the systems and the system operator
who is defending it, and demonstrate its usefulness for design.
