Optimal Deployments of Defense Mechanisms for the Internet of Things
This work addresses security vulnerabilities in IoT networks for defenders, but it is incremental as it builds on existing defense mechanisms with a hybrid optimization approach.
The authors tackled the problem of securing IoT networks against sophisticated attacks by proposing a novel approach to optimally deploy adaptive deception technology and patch management under budget constraints, using a multi-objective genetic algorithm to efficiently compute Pareto optimal deployments that maximize security and minimize cost, with results showing the GA is much more efficient than exhaustive search as IoT devices increase.
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.