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1. Logistic growth function í µí±–(í µí±¡)

1. Logistic growth function í µí±–(í µí±¡)

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Figure 1.2. NDN node operations. Source [5]
Figure 1.3. NDN packets flow. Source [6]
Figure 4.1. Iterative publications of proof-of-time
Figure 5.1. Logistic growth function í µí±–(í µí±¡)
+18 Figure 5.2. COVID-19 in SIR Model for í µí»½ = 0.5
Application of blockchain and infection processes in graphs to mitigate content poisoning attacks in information-centric networks
Thesis
Full-text available
  • Oct 2020
Information-centric networks––that are candidates for the title of the network of the future––struggle with a new vector of attacks. One of them is content poisoning, especially the stronger form––Fake Data CPA. ICN networks are especially prone to those types of attacks due to a lack of removal functionality. Currently, known authentication method...
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... [13] proposed a minimalistic stochastic model to understand the dynamics of blockchain enabled consensus on a network. Barański [14] studied mitigating the content poisoning attacks on information-centric networks. the PMF of N k G(j) the CDF of N k P (T k ≤ s) the CDF of the transmission time µ 1 the computing power of dishonest miners µ 2 the computing power of honest miners l the ratio of the time unit in the network and the time unit on the blockchain c i the community label of node i x i (t) the state of node i at time t N ...
Mitigating Misinformation Spread on Blockchain Enabled Social Media Networks
Preprint
  • Jan 2022
We construct a blockchain-enabled social media network to mitigate the spread of misinformation. We derive the information transmission-time distribution by modeling the misinformation transmission as double-spend attacks on blockchain. This distribution is then incorporated into the SIR model, which substitutes the single rate parameter in the traditional SIR model. Then, on a multi-community network, we study the propagation of misinformation numerically and show that the proposed blockchain enabled social media network outperforms the baseline network in flattening the curve of the infected population.
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