Hunting the Ethereum Smart Contract: Color-inspired Inspection of Potential Attacks
This addresses security vulnerabilities in Ethereum smart contracts, which are critical for developers and users in the blockchain community, but the approach is incremental as it builds on existing analysis methods with a novel visualization technique.
The paper tackles the problem of detecting potential attacks in Ethereum smart contracts by translating bytecode into RGB color codes and using convolutional neural networks for automatic feature extraction, achieving detection of compiler bugs without manual feature definition.
Blockchain and Cryptocurrencies are gaining unprecedented popularity and understanding. Meanwhile, Ethereum is gaining a significant popularity in the blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contract and decentralized applications (Dapps). This new paradigm of applications opens the door to many possibilities and opportunities. However, the security of Ethereum smart contracts has not received much attention; several Ethereum smart contracts malfunctioning have recently been reported. Unlike many previous works that have applied static and dynamic analyses to find bugs in smart contracts, we do not attempt to define and extract any features; instead we focus on reducing the expert's labor costs. We first present a new in-depth analysis of potential attacks methodology and then translate the bytecode of solidity into RGB color code. After that, we transform them to a fixed-sized encoded image. Finally, the encoded image is fed to convolutional neural network (CNN) for automatic feature extraction and learning, detecting compiler bugs of Ethereum smart contract.