  Ethereum smart contracts are programs that run on the Ethereum blockchain,
and many smart contract vulnerabilities have been discovered in the past
decade. Many security analysis tools have been created to detect such
vulnerabilities, but their performance decreases drastically when codes to be
analyzed are being rewritten. In this paper, we propose Eth2Vec, a
machine-learning-based static analysis tool for vulnerability detection, with
robustness against code rewrites in smart contracts. Existing
machine-learning-based static analysis tools for vulnerability detection need
features, which analysts create manually, as inputs. In contrast, Eth2Vec
automatically learns features of vulnerable Ethereum Virtual Machine (EVM)
bytecodes with tacit knowledge through a neural network for language
processing. Therefore, Eth2Vec can detect vulnerabilities in smart contracts by
comparing the code similarity between target EVM bytecodes and the EVM
bytecodes it already learned. We conducted experiments with existing open
databases, such as Etherscan, and our results show that Eth2Vec outperforms the
existing work in terms of well-known metrics, i.e., precision, recall, and
F1-score. Moreover, Eth2Vec can detect vulnerabilities even in rewritten codes.
