A deep learning-based vulnerability detection in blockchain smart contracts using masked attention and control flow graph analysis
SCs are self-executing programs on the blockchain, used for transactions without intermediaries, particularly in cryptocurrencies like Ethereum. However, they are vulnerable to security flaws that can lead to significant financial losses, as demonstrated by the DAO hack 2016. Common vulnerabilities include re-entrancy errors, timestamp dependency, infinite loops, and integer overflows. Detecting these flaws is crucial but complex due to the immutable nature of the blockchain and the complexity of the contracts. Therefore, developing techniques for analyzing, testing, and verifying the security of SCs is essential to ensure their reliability and safety. This work presents a novel approach to detecting vulnerabilities in SCs using deep learning.