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.

Being the largest Initial Coin Offering project, EOSIO has attracted great interest in cryptocurrency markets. Despite its popularity and prosperity (e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5, 2020), there is almost no work investigating the EOSIO token ecosystem. To fill this gap, we are the first to conduct a systematic investigation of the EOSIO token ecosystem by conducting a comprehensive graph analysis of the entire on-chain EOSIO data (nearly 135 million blocks). We construct token-creator graphs, token-contract creator graphs, token-holder graphs, and token-transfer graphs to characterize token creators, holders, and transfer activities. Through graph analysis, we have obtained many insightful findings and observed some abnormal trading patterns. Moreover, we propose a fake-token detection algorithm to identify tokens generated by fake users or fake transactions and analyze their corresponding manipulation behaviors. Evaluation results also demonstrate the effectiveness of our algorithm.

As one of the most popular blockchain systems, EOSIO has been widely used in decentralized applications (DApps). Compared with traditional proof-of-work (PoW)-based blockchain systems like Bitcoin, EOSIO achieves a high transaction throughput and an alleged decentralization with the Delegated Proof-of-Stake (DPoS) consensus protocol. However, recent reports claimed the existence of voting collusion and manipulation during the DPoS consensus procedure of EOSIO, which may greatly decline the decentralization degree, fault tolerance, and reliability of the whole system. In this article, we obtain data from up to 135 000 000 blocks of EOSIO and conduct a data-driven decentralization analysis. Specifically, we characterize the decentralization evolution of the two phases in DPoS, namely block producer election and block production. Moreover, we study the voters with similar voting behaviors and propose methods to discover abnormal mutual voting behaviors in EOSIO. The analysis results show how EOSIO gradually evolves from decentralization to oligopoly and our methods can effectively capture abnormal voting phenomena in the EOSIO, which can also provide important insights for the design and maintenance of other DPoS-based blockchains.

Blockchain has attracted lots of attention in recent years. However, the performance of blockchain cannot meet the requirement of massive Internet of Things (IoT) devices. One of the important bottlenecks of blockchain is the limited computing resources on a single server while executing transactions. To address this issue, we propose Aeolus blockchain to achieve the distributed execution of blockchain transactions. There are two key challenges to achieving this for IoT blockchain: transaction structure and state consistency. Facing these challenges, we first propose a distributed blockchain transaction structure, which imports extra parameters to divide the transaction execution into different stages to enable distributed execution. Second, we propose distributed state update sharding, which equips each blockchain peer with its own master and shard servers. In this way, each blockchain peer can be considered as a cluster that distributes the transaction to shorten the processing time and reach the consensus finally. We implement Aeolus on Go-Ethereum to evaluate its feasibility, on a testbed including 132 cloud servers. Our system runs stably for more than 8 h under the workload of 190 000 000 real-world user transactions. Experimental results show the efficiency that Aeolus can achieve more than 100 000 transactions/s of blockchain transactions, which is 15.6 times the throughput of the original blockchain.