论文标题
主动防御涉及国际互联网：将移动目标防御与网络束缚整合
Proactive Defense for Internet-of-Things: Integrating Moving Target Defense with Cyberdeception
论文作者
论文摘要
攻击者很可能会损害资源限制的互联网（IOT）设备，因为强大的安全保护可能不适合部署。这需要一种替代方法来保护物联网网络中的脆弱组件。在本文中，我们提出了一种综合防御技术，以通过利用网络对象（即诱饵系统）和移动目标防御（即网络拓扑改组）来实现入侵预防。我们根据软件定义的网络（SDN）的IoT网络中的图形安全模型在分析上验证我们提出的技术的有效性和效率。 We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address "when" to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address "how" to perform network topology shuffling on a decoy-populated IoT network, and analyze which strategy can best achieve a system goal such as prolonging the system lifetime, maximizing deception effectiveness,最大化服务可用性或最大程度地减少国防成本。我们的结果表明，在最佳参数设定下，软件定义了运行入侵预防技术的软件网络延长了系统寿命，增加了损害关键节点的攻击复杂性，并与同行Iot网络相比，保持了卓越的服务可用性，而无需运行我们的入侵预防技术。此外，当给定一个目标或多目标目标（例如，最大化系统寿命和服务可用性的同时最大程度地减少国防成本）作为输入时，“如何”和“如何”策略是确定执行我们提出的技术的最佳组合，在该技术下可以最好地实现指定的目标。
Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this paper, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We verify the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address "when" to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address "how" to perform network topology shuffling on a decoy-populated IoT network, and analyze which strategy can best achieve a system goal such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. Our results demonstrate that a software defined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of "how" and "how" strategies is identified for executing our proposed technique under which the specified goal can be best achieved.
