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Exploiting smart contract bytecode for classification on ethereum

: Sezer, S.; Eyhoff, C.; Prinz, W.; Rose, T.

Fulltext ()

Asprion, P.M.:
PoEM-WS 2020, Practice of Enterprise Modelling Workshops 2020. Online resource : Proceedings of the workshops co-organized with the 13th IFIP WG 8.1 working conference on the Practice of Enterprise Modelling (PoEM 2020). On-line (originally located in Riga, Latvia), November 26, 2020
La Clusaz: CEUR, 2020 (CEUR Workshop Proceedings 2749)
ISSN: 1613-0073
URN: urn:nbn:de:0074-2749-5
Working Conference on the Practice of Enterprise Modeling (PoEM) <13, 2020, Online>
Conference Paper, Electronic Publication
Fraunhofer FIT ()

Due to the increase in smart contracts in Ethereum, a need for proper classification has emerged. Although the smart contracts are accessible due to the open nature of the Blockchain, readability is still an issue with respect to the smart contract bytecode. We propose an automated approach for classifying smart contracts that utilize popular text classification methods on the opcode translation of the smart contract bytecode in order to overcome this limitation. Our experiments indicate that the decision-tree-based techniques like Random Forest and Xgboost outmatch the traditional classification tools like Naïve Bayes, Logistic Regression, and SVM once the opcode input is presented as n-gram tf-idf vectors.