The cryptocurrency is attracting more and more attention because of the blockchain technology. Ethereum is gaining a significant popularity in blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contracts and decentralized applications (Dapps). There are many kinds of cryptocurrency information on the social network. The risks and fraud problems behind it have pushed many countries including the United States, South Korea, and China to make warnings and set up corresponding regulations. However, the security of Ethereum smart contracts has not gained much attention. Through the Deep Learning approach, we propose a method of sentiment analysis for Ethereum's community comments. In this research, we first collected the users' cryptocurrency comments from the social network and then fed to our LSTM + CNN model for training. Then we made prediction through sentiment analysis. With our research result, we have demonstrated that both the precision and the recall of sentiment analysis can achieve 0.80+. More importantly, we deploy our sentiment analysis1 on RatingToken and Coin Master (mobile application of Cheetah Mobile Blockchain Security Center23). We can effectively provide detail information to resolve the risks of being fake and fraud problems.
Elvira Albert, Pablo Gordillo, Albert Rubio, Ilya Sergey
Gas is a measurement unit of the computational effort that it will take to execute every single operation that takes part in the Ethereum blockchain platform. Each instruction executed by the Ethereum Virtual Machine (EVM) has an associated gas consumption specified by Ethereum. If a transaction exceeds the amount of gas allotted by the user (known as gas limit), an out-of-gas exception is raised. There is a wide family of contract vulnerabilities due to out-of-gas behaviours. We report on the design and implementation of GASTAP, a Gas-Aware Smart contracT Analysis Platform, which takes as input a smart contract (either in EVM, disassembled EVM, or in Solidity source code) and automatically infers sound gas upper bounds for all its public functions. Our bounds ensure that if the gas limit paid by the user is higher than our inferred gas bounds, the contract is free of out-of-gas vulnerabilities.
Shuqing Bian, Zhenpeng Deng, Fei Li, Will Monroe · 12 authors
Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing astonishing profits to investors. Unlike other money and banking systems, most digital tokens do not require central authorities. Being decentralized poses significant challenges for credit rating. Most ICOs are currently not subject to government regulations, which makes a reliable credit rating system for ICO projects necessary and urgent. In this paper, we introduce IcoRating, the first learning--based cryptocurrency rating system. We exploit natural-language processing techniques to analyze various aspects of 2,251 digital currencies to date, such as white paper content, founding teams, Github repositories, websites, etc. Supervised learning models are used to correlate the life span and the price change of cryptocurrencies with these features. For the best setting, the proposed system is able to identify scam ICO projects with 0.83 precision. We hope this work will help investors identify scam ICOs and attract more efforts in automatically evaluating and analyzing ICO projects.