DAppCheat: Detecting Cheating Robots for DApps on Multiple Blockchains

Abstract

Recent years have witnessed a rapid increase in the number of blockchain-based decentralized applications (DApps). As reported by DAppRadar, there are more than 5,000 DApps with more than 17.2 million daily Unique Active Wallets (users). However, it is also reported that some robots are used to manipulate the ranking, attract more users, cheat investors, etc. Hence, it is necessary to detect those robots. Unlike traditional robots or spam detection on Internet, each blockchain has its specific data structure with the impacts of exchanges and whales, leading to the challenges of detecting DApp robots. In this paper, we conduct the first systematic investigation on DApps robots, named DAppCheat. We first collect and release the first multi-blockchain DApp-user dataset, including 4,857 DApps and 99,758,959 users from Ethereum, EOSIO, TRON, and BSC. We propose a general parent account mechanism for multiple blockchains in order to find anonymous user collusion. We define the creating account weight and the used DApp volume weight to reduce the impacts of exchanges and whales. Extensive experimental results show the effectiveness of DAppCheat.