The purpose of this newly published research by Xueyan Tang is to investigate the use of deep learning in the identification of smart contract vulnerabilities. The research was co-authored by Yuying Du, Alan Lai, Ze Zhang and Lingzhi Shi.
Blockchain technology relies heavily on smart contracts, which are critical to the creation of decentralized apps. On the other hand, system failures and monetary losses may result from smart contract weaknesses. Static analysis tools are often used to find vulnerabilities in smart contracts, however because of their strong dependence on preset criteria and limited ability to do semantic analysis, they frequently produce false positives and false negatives.
These pre-established rules also fail to generalize or adapt to new facts, and they go out of date very rapidly. Deep learning techniques, on the other hand, may learn the characteristics of vulnerabilities during the training process and do not need predetermined detection algorithms.
Our proposed
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