Security agencies in the real world often need to
protect targets with time-dependent values, e.g.,
tourist sites where the number of travelers changes
over time. Since the values of different targets often change asynchronously, the defender can relocate security resources among targets dynamically
to make the best use of limited resources. We propose a game-theoretic scheme to develop dynamic,
randomized security strategies in consideration of
adversary’s surveillance capability. This differs
from previous studies on security games by considering varying target values and continuous strategy spaces of the security agency and the adversary. The main challenge lies in the computational
intensiveness due to the continuous, hence infinite
strategy spaces. We propose an optimal algorithm
and an arbitrarily near-optimal algorithm to compute security strategies under different conditions.
Experimental results show that both algorithms significantly outperform existing approaches.