Security is an important concern worldwide. Stackelberg Security Games have been used successfully in a variety of security applications, to optimally schedule limited defense resources by modeling the interaction between attackers and defenders. Prior research has suggested that it is possible to classify adversary behavior into distinct groups of adversaries based on the ways humans explore their decision alternatives. However, despite the widespread use of Stackelberg Security Games, there has been little research on how adversaries adapt to defense strategies over time (i.e., dynamics of behavior). In this paper, we advance this work by showing how adversaries' behavior changes as they learn the defenders' behavior over time. Furthermore, we show how behavioral game theory models can be modified to capture learning dynamics using a Bayesian Updating modeling approach. These models perform similarly to a cognitive model known as Instance-Based-Learning to predict learning patterns.