Simulação do Penny Attack no Ethereum e sua Identificação usando Classificadores

• José Eduardo de Azevedo Sousa Federal University of Juiz de Fora https://orcid.org/0000-0001-5658-0292
• Vinicius Oliveira Federal University of Juiz de Fora
• Julia Almeida Valadares Federal University of Juiz de Fora
• Alex Borges Vieira Federal University of Juiz de Fora https://orcid.org/0000-0003-0821-126X
• Heder Soares Bernardino Federal University of Juiz de Fora https://orcid.org/0000-0003-2012-7802
• Saulo Moraes Villela Federal University of Juiz de Fora https://orcid.org/0000-0001-5958-4766
• Glauber Dias Gonçalves Federal University of Piauí https://orcid.org/0000-0002-0742-4346

DOI: https://doi.org/10.5753/wblockchain.2020.12438

Abstract

The growing interest in Ethereum leads to concerns in its security, given that there have already been attacks that exploited its charging mechanism or Penny Attack. These attacks affected the network, causing transactions congestion and there are indications that Ethereum remains susceptible to this type of attack. We analyze the behavior of the network during a Penny Attack, assuming its negligible cost to the attacker and seeking machine learning techniques to detect it previously, using transaction attributes. Our techniques had an AUC, Fb and recall greater than 94%, 82% and 98% respectively.

Keywords: Ethereum, DDoS, Comitê, Aprendizado de máquina

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Sousa, J. E. d. A., Oliveira, V., Valadares, J., Vieira, A. B., Villela, S. M., Bernardino,H.sS., and Dias, G. (2020). An analysis of the fees and pending time correlation inethereum. International Journal of Network Management nem.2113.