A distributed adaptive routing against selective forwarding attack in scale-free network considering cascading failure

IF 2.2 4区 数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Rong-rong Yin, Xuyao Ma, Huaili Yuan, Mengfa Zhai, Changjiang Guo
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引用次数: 0

Abstract

To address the issues of data insecurity and unreliable transmission, redundancy-based data recovery can guarantee data security, but the increase of redundant data will reduce the robustness of network in the face of cascading failures. A distributed adaptive routing method in scale-free network is proposed to improve network resilience against selective forwarding attacks and the robustness against cascading failures. Based on the polynomial principle, the proposed routing method slices packets, adds redundancy reasonably and adopts multipath sequential routing method to completely send data to the destination node. The ability to resist selective forwarding attacks and robustness against cascading failures is investigated and analysed throughout the entire network operation. Simulation results show that our proposed routing method is not restricted by the number of disjoint paths, can maintain a higher data recovery ratio and resist effectively selective forwarding attacks, and also balances the network load well. Moreover, this routing method has a lower end-to-end latency for data transmission and is highly resistant to cascading failures under random and intentional attacks.
考虑级联故障的无标度网络中抗选择性转发攻击的分布式自适应路由
为了解决数据不安全和传输不可靠的问题,基于冗余的数据恢复可以保证数据的安全性,但面对级联故障,冗余数据的增加会降低网络的鲁棒性。为了提高网络对选择性转发攻击的弹性和对级联故障的鲁棒性,提出了一种无标度网络中的分布式自适应路由方法。该路由方法基于多项式原理,对数据包进行切片,合理增加冗余,采用多径顺序路由方式将数据完整地发送到目的节点。在整个网络运行中,研究和分析了抵抗选择性转发攻击和抗级联故障的鲁棒性的能力。仿真结果表明,本文提出的路由方法不受不连接路径数量的限制,能够保持较高的数据恢复率,有效抵御选择性转发攻击,并能很好地平衡网络负载。此外,这种路由方式具有较低的端到端数据传输延迟,并且具有很强的抗随机攻击和故意攻击下的级联故障能力。
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来源期刊
Journal of complex networks
Journal of complex networks MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.20
自引率
9.50%
发文量
40
期刊介绍: Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks i.e. online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine related networks - Animal social networks - Climate networks - Cognitive, language and informational network
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