# Optimal Deployments of Defense Mechanisms for the Internet of Things

## TL;DR

This paper introduces a multi-objective genetic algorithm approach to optimally deploy defense mechanisms like deception technology and patch management in IoT networks, balancing security and cost.

## Contribution

It presents a novel multi-objective genetic algorithm framework for optimal defense deployment in IoT, outperforming exhaustive search in efficiency and scalability.

## Key findings

- Genetic algorithm effectively finds Pareto optimal deployments.
- GA outperforms exhaustive search in runtime and scalability.
- Proposed approach enhances IoT security with cost-effective strategies.

## Abstract

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.