A hierarchical virus immunization method for community networks

Fu, Cai; Li, Min; Zhang, Yunhe; Zou, Deqing; Han, Lansheng

doi:10.1109/cc.2014.6969779

Cited by 2 publications

(1 citation statement)

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“…The logical centralization of network intelligence presents exciting challenges and opportunities to enhance security in such networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. Malicious code detection and prevention under the new architecture need further study [7][8][9][10][11][12][13][14].…”

Section: Research Trends and Gapsmentioning

confidence: 99%

Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

Liu

Ren

et al. 2017

Security and Communication Networks

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As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q<qc. The results showed that our model is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

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