An adaptive agent-based simulation modeling technology has been developed that allows us to build, for example, simulated decision makers representing defenders and attackers of a computer system engaged in cyberwarfare in their simulated microworld. The adaptive adversaries coevolve: attackers evolve new attack patterns and overcome cyber defenses, and defenders subsequently evolve new defensive patterns to the attacks. When we run these adaptive decision-maker models, we see what looks like human adversarial behavior. These simulated attackers learn to time their attacks just as real-world hackers do with virus attacks. Simulated defenders soon catch on and resynchronize their defenses to match the timing of these attacks. This adaptive simulation modeling can automatically discover new behaviors beyond those that were initially built into the models, providing a more realistic simulation of intelligent behavior. Such models provide both an opportunity to discover novel adversarial behavior, and a testbed for other adversary course of action prediction models.