Optimal Deployments of Defense Mechanisms for the Internet of Things

Mengmeng Ge, Jin-Hee Cho, C. Kamhoua, Dong Seong Kim
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引用次数: 2

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.
物联网防御机制的优化部署
攻击者可以利用物联网(IoT)设备作为入口点,在未被早期发现的情况下闯入物联网网络。很少有工作采用混合方法,以最佳方式结合不同的防御机制,以提高物联网的安全性,抵御复杂的攻击。在这项工作中,我们提出了一种新的方法,在预算约束下为物联网生成自适应欺骗技术的战略部署和补丁管理解决方案。我们使用图形安全模型和三个评估指标来衡量所提出的防御机制的有效性和效率。我们应用多目标遗传算法(GA)计算防御机制的{\em Pareto最优}部署,以实现安全性最大化和部署成本最小化。我们提出了一个案例研究来展示所提出方法的可行性,并为防御者提供各种方法来选择物联网防御机制的最佳部署。我们将遗传算法与穷举搜索算法(ESA)在运行时复杂度和最优性的性能准确性方面进行了比较。我们的结果表明,与物联网设备的增加成比例,GA在计算良好的部署分布方面比ESA更有效。
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