  The present survey aims at presenting the current machine learning techniques
employed in security games domains. Specifically, we focused on papers and
works developed by the Teamcore of University of Southern California, which
deepened different directions in this field. After a brief introduction on
Stackelberg Security Games (SSGs) and the poaching setting, the rest of the
work presents how to model a boundedly rational attacker taking into account
her human behavior, then describes how to face the problem of having attacker's
payoffs not defined and how to estimate them and, finally, presents how online
learning techniques have been exploited to learn a model of the attacker.
