Research on DDoS Attack Detection in Software Defined Network

Ma, Zongwen; Zhao, Gansen; Wei-wen, Li; Mo, Zefeng; Wang, Xinming; Chen, Bingchuan; Lin, Cheng-Xian

doi:10.1109/iccbb.2018.8756439

Cited by 2 publications

(1 citation statement)

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“…Similarly, we can use Minkowski Distance, which is a generalization of Euclidean Distance. Minkowski Distance [61], [62] with parameter r is defined as:…”

Section: B Governance Layermentioning

confidence: 99%

Measuring Decentrality in Blockchain Based Systems

et al. 2020

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Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network.

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“…Similarly, we can use Minkowski Distance, which is a generalization of Euclidean Distance. Minkowski Distance [61], [62] with parameter r is defined as:…”

Section: B Governance Layermentioning

confidence: 99%