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FlipIn: A Game-Theoretic Cyber Insurance Framework for Incentive-Compatible Cyber Risk Management of Internet of Things

机译:FlipIn:激励兼容的物联网网络风险管理的博弈论网络保险框架

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Internet of Things (IoT) is highly vulnerable to emerging Advanced Persistent Threats (APTs) that are often operated by well-resourced adversaries. Achieving perfect security for IoT networks is often cost-prohibitive if not impossible. Cyber insurance is a valuable mechanism to mitigate cyber risks for IoT systems. In this work, we propose a bi-level game-theoretic framework called FlipIn to design incentive-compatible and welfare-maximizing cyber insurance contracts. The framework captures the strategic interactions among APT attackers, IoT defenders, and cyber insurance insurers, and incorporates influence networks to assess the systemic cyber risks of interconnected IoT devices. The FlipIn framework formulates a game over networks within a principal-agent problem of moral-hazard type to design a cyber risk-aware insurance contract. We completely characterize the equilibrium solutions of the bi-level games for a network of distributed defenders and a semi-homogeneous centralized defender and show that the optimal insurance contracts cover half of the defenders losses. Our framework predicts the risk compensation of defenders and the Peltzman effect of insurance. We study a centralized security management scenario and its decentralized counterpart, and leverage numerical experiments to show that network connectivity plays an important role in the security of the IoT devices and the insurability of both distributed and centralized defenders.
机译:物联网(IoT)极易受到新兴的持久性威胁(APT)的攻击,这些威胁通常由资源丰富的对手进行操作。如果不是不可能的话,为物联网网络实现完美的安全性通常会导致成本过高。网络保险是缓解物联网系统网络风险的宝贵机制。在这项工作中,我们提出了一个名为FlipIn的双层博弈论框架,以设计激励兼容和福利最大化的网络保险合同。该框架捕获了APT攻击者,物联网防御者和网络保险公司之间的战略互动,并合并了影响力网络以评估互连的物联网设备的系统性网络风险。 FlipIn框架在道德风险类型的委托人问题内制定了网络游戏,以设计网络风险意识保险合同。我们完整地描述了分布式防御者网络和半均质集中防御者网络的两级博弈的均衡解,并表明最优保险合同覆盖了防御者损失的一半。我们的框架可预测防御者的风险补偿和保险的佩尔兹曼效应。我们研究了集中式安全管理方案及其分散的对等方案,并利用数值实验表明网络连接在IoT设备的安全性以及分布式和集中式防御者的可保性中发挥着重要作用。

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