  Internet of Things (IoT) devices can be exploited by the attackers as entry
points to break into the IoT networks without early detection. Little work has
taken hybrid approaches that combine different defense mechanisms in an optimal
way to increase the security of the IoT against sophisticated attacks. In this
work, we propose a novel approach to generate the strategic deployment of
adaptive deception technology and the patch management solution for the IoT
under a budget constraint. We use a graphical security model along with three
evaluation metrics to measure the effectiveness and efficiency of the proposed
defense mechanisms. We apply the multi-objective genetic algorithm (GA) to
compute the {\em Pareto optimal} deployments of defense mechanisms to maximize
the security and minimize the deployment cost. We present a case study to show
the feasibility of the proposed approach and to provide the defenders with
various ways to choose optimal deployments of defense mechanisms for the IoT.
We compare the GA with the exhaustive search algorithm (ESA) in terms of the
runtime complexity and performance accuracy in optimality. Our results show
that the GA is much more efficient in computing a good spread of the
deployments than the ESA, in proportion to the increase of the IoT devices.
