  A model of strategy formulation is used to study how an adaptive attacker
learns to overcome a moving target cyber defense. The attacker-defender
interaction is modeled as a game in which a defender deploys a temporal
platform migration defense. Against this defense, a population of attackers
develop strategies specifying the temporal ordering of resource investments
that bring targeted zero-day exploits into existence. Attacker response to two
defender temporal platform migration scheduling policies are examined. In the
first defender scheduling policy, the defender selects the active platform in
each match uniformly at random from a pool of available platforms. In the
second policy the defender schedules each successive platform to maximize the
diversity of the source code presented to the attacker. Adaptive attacker
response strategies are modeled by finite state machine (FSM) constructs that
evolve during simulated play against defender strategies via an evolutionary
algorithm. It is demonstrated that the attacker learns to invest heavily in
exploit creation for the platform with the least similarity to other platforms
when faced with a diversity defense, while avoiding investment in exploits for
this least similar platform when facing a randomization defense. Additionally,
it is demonstrated that the diversity-maximizing defense is superior for
shorter duration attacker-defender engagements, but performs sub-optimally in
extended attacker-defender interactions.
