Exploiting smart contract bytecode for classification on ethereum

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 Naive Bayes, Logistic Regression, and SVM once the opcode input is presented as n-gram tf-idf vectors.