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Scalable Network Diversity Modeling For Assessing Threats in Cloud Networks

Shetty, Sachin ; Song, Min ; Yuchi, Xuebiao

Moving Target Defense for Distributed Systems, 2016, p.25-42 [Periódico revisado por pares]

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Título:
Scalable Network Diversity Modeling For Assessing Threats in Cloud Networks
Autor: Shetty, Sachin ; Song, Min ; Yuchi, Xuebiao
Assuntos: Attack Graph ; Attack Scenario ; Closeness Centrality ; Communications engineering / telecommunications ; Computer security ; Network Diversity ; Network hardware ; Network System
É parte de: Moving Target Defense for Distributed Systems, 2016, p.25-42
Descrição: Network diversity based security metric is attracting increasing interest in cybersecurity research community. There have been several efforts towards network diversity modeling, for the purpose of evaluating a network’s robustness against potential attacks. However, those efforts commonly use traditional network resource graph abstraction to model network diversity, which are not scalable when applied to modern large scaled networked systems, which can be encountered in cloud environments. In this chapter, we introduce a hierarchical network resource graph abstraction method to improve the scalability of network diversity modeling. Specifically, we use a two-layer hierarchy to separate the network topology information (in the upper layer) from the resource information of each host (in the lower layer). Simulations show that the proposed approach is scalable for larger sized networked systems.
Títulos relacionados: Wireless Networks
Editor: Switzerland: Springer International Publishing AG
Idioma: Inglês

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