  Modeling attacks, in which an adversary uses machine learning techniques to
model a hardware-based Physically Unclonable Function (PUF) pose a great threat
to the viability of these hardware security primitives. In most modeling
attacks, a random subset of challenge-response-pairs (CRPs) are used as the
labeled data for the machine learning algorithm. Here, for the arbiter-PUF, a
delay based PUF which may be viewed as a linear threshold function with random
weights (due to manufacturing imperfections), we investigate the role of active
learning in Support Vector Machine (SVM) learning. We focus on challenge
selection to help SVM algorithm learn ``fast'' and learn ``slow''. Our methods
construct challenges rather than relying on a sample pool of challenges as in
prior work. Using active learning to learn ``fast'' (less CRPs revealed, higher
accuracies) may help manufacturers learn the manufactured PUFs more
efficiently, or may form a more powerful attack when the attacker may query the
PUF for CRPs at will. Using active learning to select challenges from which
learning is ``slow'' (low accuracy despite a large number of revealed CRPs) may
provide a basis for slowing down attackers who are limited to overhearing CRPs.
