A Data Science Approach for Honeypot Detection in Ethereum

Ethereum smart contracts have recently drawn a considerable amount of attention from the media, the financial industry and academia. With the increase in popularity, malicious users found new opportunities to profit from deceiving newcomers. Consequently, attackers started luring other attackers into contracts that seem to have exploitable flaws, but that actually contain a complex hidden trap that in the end benefits the contract creator. This kind of contracts are known in the blockchain community as Honeypots. A recent study, proposed to investigate this phenomenon by focusing on the contract bytecode using symbolic analysis. In this paper, we present a data science approach based on the contract transaction behavior. We create a partition of all the possible cases of fund movement between the contract creator, the contract, the sender of the transaction and other participants. We calculate the frequency of every case per contract, and extract as well other contract features and transaction aggregated features. We use the collected information to train machine learning models that classify contracts as honeypot or non-honeypots, and also measure how well they perform when classifying unseen honeypot types. We compare our results with the bytecode analysis method using labels from a previous study, and discuss in which cases each solution has advantages over the other.