复杂网络中随机攻击和故意攻击的序贯防御
Sequential Defense Against Random and Intentional Attacks in Complex Networks
摘要：
网络抗攻击鲁棒性是网络科学中最基本的研究之一，因为它与各种网络范式的可靠性和功能性密切相关。然而，尽管对节点删除的内在拓扑脆弱性的研究，很少有人知道网络的鲁棒性时，网络防御机制的实施，特别是对网络工程系统配备的检测能力。本文首先提出了一种复杂网络中的顺序防御机制，用于攻击推理和脆弱性评估，其中数据融合中心根据网络中节点报告的二进制攻击状态顺序地推断攻击的存在。在两种主要攻击方案下，网络鲁棒性是根据在网络中断之前识别攻击的能力来评估的，即，随机和故意攻击。我们提供了一个参数插件模型的性能评估所提出的机制，并通过规范的复杂网络模型和现实世界的大规模网络拓扑结构验证其有效性和可靠性。实验结果表明，顺序防御机制通过获取网络中一小部分节点的有限攻击状态信息，极大地提高了网络的鲁棒性，降低了网络中断的可能性。
英文摘要：
Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is firstly proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.
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