  Currently, smart contract vulnerabilities (SCVs) have emerged as a major
factor threatening the transaction security of blockchain. Existing
state-of-the-art methods rely on deep learning to mitigate this threat. They
treat each input contract as an independent entity and feed it into a deep
learning model to learn vulnerability patterns by fitting vulnerability labels.
It is a pity that they disregard the correlation between contracts, failing to
consider the commonalities between contracts of the same type and the
differences among contracts of different types. As a result, the performance of
these methods falls short of the desired level.
  To tackle this problem, we propose a novel Contrastive Learning Enhanced
Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear.
In particular, Clear employs a contrastive learning (CL) model to capture the
fine-grained correlation information among contracts and generates correlation
labels based on the relationships between contracts to guide the training
process of the CL model. Finally, it combines the correlation and the semantic
information of the contract to detect SCVs. Through an empirical evaluation of
a large-scale real-world dataset of over 40K smart contracts and compare 13
state-of-the-art baseline methods. We show that Clear achieves (1) optimal
performance over all baseline methods; (2) 9.73%-39.99% higher F1-score than
existing deep learning methods.
