  Nowadays, security activities in smart contracts concentrate on vulnerability
detection. Despite early success, we find that developers' intent to write
smart contracts is a more noteworthy security concern because smart contracts
with malicious intent have caused significant users' financial loss.
Unfortunately, current approaches to identify the aforementioned malicious
smart contracts rely on smart contract security audits, which entail huge
manpower consumption and financial expenditure. To resolve this issue, we
propose a novel deep learning-based approach, SmartIntentNN, to conduct
automated smart contract intent detection. SmartIntentNN consists of three
primary parts: a pre-trained sentence encoder to generate the contextual
representations of smart contracts, a K-means clustering method to highlight
intent-related representations, and a bidirectional LSTM-based (long-short term
memory) multi-label classification network to predict the intents in smart
contracts. To evaluate the performance of SmartIntentNN, we collect more than
40,000 real smart contracts and perform a series of comparison experiments with
our selected baseline approaches. The experimental results demonstrate that
SmartIntentNN outperforms all baselines by up to 0.8212 in terms of the
f1-score metric.
