Exploiting Bytecode Analysis for Reentrancy Vulnerability Detection in Ethereum Smart
Contracts.
[2023]
Reentrancy is a type of attack that can occur in smart contracts, enabling untrusted external code execution within the contract. We introduce a straightforward and lightweight approach to address these limitations in reentrancy detection. Our approach employs an image-based detection method utilizing deep learning. The pipeline of our method involves disassembling the smart contracts into opcodes and transforming them into RGB images. These images are then used to train a VGG16 CNN model to detect similarities between images labeled as either “Vulnerable” or “Not Vulnerable”. To address class imbalance, we implement image augmentation techniques to expand the training ataset. Experimental results conducted on a publicly available dataset demonstrate that our model achieves a significantly high accuracy rate of 99.07%.