  The results of a learning process depend on the input data. There are cases
in which an adversary can strategically tamper with the input data to affect
the outcome of the learning process. While some datasets are difficult to
attack, many others are susceptible to manipulation. A resourceful attacker can
tamper with large portions of the dataset and affect them. An attacker can
additionally strategically focus on a preferred subset of the attributes in the
dataset to maximize the effectiveness of the attack and minimize the resources
allocated to data manipulation. In light of this vulnerability, we introduce a
solution according to which the defender implements an array of learners, and
their activation is performed strategically. The defender computes the (game
theoretic) strategy space and accordingly applies a dominant strategy where
possible, and a Nash-stable strategy otherwise. In this paper we provide the
details of this approach. We analyze Nash equilibrium in such a strategic
learning environment, and demonstrate our solution by specific examples.
