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Epidemie survivability: Characterizing networks under epidemic-like failure propagation scenarios

机译:流行病的生存能力:表征类似流行病的故障传播场景下的网络

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Epidemics theory has been used in different contexts in order to describe the propagation of diseases, human interactions or natural phenomena. In computer science, virus spreading has been also characterized using epidemic models. Although in the past the use of epidemic models in telecommunication networks has not been extensively considered, nowadays, with the increasing computation capacity and complexity of operating systems of modern network devices (routers, switches, etc.), the study of possible epidemic-like failure scenarios must be taken into account. When epidemics occur, such as in other multiple failure scenarios, identifying the level of vulnerability offered by a network is one of the main challenges. In this paper, we present epidemic survivability, a new network measure that describes the vulnerability of each node of a network under a specific epidemic intensity. Moreover, this metric is able to identify the set of nodes which are more vulnerable under an epidemic attack. In addition, two applications of epidemic survivability are provided. First, we introduce epidemic criticality, a novel robustness metric for epidemic failure scenarios. A case study shows the utility of this new metric comparing several network topologies and epidemic intensities. Then, two immunization strategies are proposed: high epidemic survivability (HES) and high epidemic survivability adaptive (HESA). The presented results show that network vulnerability can be significantly reduced by using our proposals, compared to other well-known existing methods.
机译:流行病学理论已用于不同背景下,以描述疾病,人类相互作用或自然现象的传播。在计算机科学中,还使用流行病模型对病毒传播进行了表征。尽管过去并未广泛考虑在电信网络中使用流行病模型,但如今,随着现代网络设备(路由器,交换机等)操作系统的计算能力和复杂性的提高,对类似流行病的研究必须考虑故障情况。当发生流行病时(例如在其他多个故障情况下),确定网络提供的漏洞级别是主要挑战之一。在本文中,我们介绍了流行性生存能力,这是一种新的网络度量,它描述了特定流行强度下网络每个节点的脆弱性。而且,该度量能够识别在流行病攻击下更易受攻击的节点集。另外,提供了流行性生存能力的两种应用。首先,我们介绍流行病的关键性,这是一种针对流行病失败情况的新颖的稳健性指标。案例研究表明,该新指标可用于比较几种网络拓扑和流行强度。然后,提出了两种免疫策略:高流行性生存能力(HES)和高流行性生存能力适应性(HESA)。呈现的结果表明,与其他众所周知的现有方法相比,使用我们的建议可以大大减少网络漏洞。

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