  Game theory is appropriate for studying cyber conflict because it allows for
an intelligent and goal-driven adversary. Applications of game theory have led
to a number of results regarding optimal attack and defense strategies.
However, the overwhelming majority of applications explore overly simplistic
games, often ones in which each participant's actions are visible to every
other participant. These simplifications strip away the fundamental properties
of real cyber conflicts: probabilistic alerting, hidden actions, unknown
opponent capabilities.
  In this paper, we demonstrate that it is possible to analyze a more realistic
game, one in which different resources have different weaknesses, players have
different exploits, and moves occur in secrecy, but they can be detected.
Certainly, more advanced and complex games are possible, but the game presented
here is more realistic than any other game we know of in the scientific
literature. While optimal strategies can be found for simpler games using
calculus, case-by-case analysis, or, for stochastic games, Q-learning, our more
complex game is more naturally analyzed using the same methods used to study
other complex games, such as checkers and chess. We define a simple evaluation
function and ploy multi-step searches to create strategies. We show that such
scenarios can be analyzed, and find that in cases of extreme uncertainty, it is
often better to ignore one's opponent's possible moves. Furthermore, we show
that a simple evaluation function in a complex game can lead to interesting and
nuanced strategies.
