Exploring Heterogeneous Decentralized Markets in DeFi and NFT on Ethereum Blockchain

Blockchain applications have grown tremendously recently, especially in the Decentralized Finance (DeFi) and Non-fungible Token (NFT) markets. The DeFi and NFT markets generate massive transactions and research-worthy data. However, few studies have systematically processed and analyzed them, preventing users from understanding the ecosystem. The main challenge of analyzing the DeFi & NFT markets is the heterogeneity of data that different markets have heterogeneous businesses and data. To address this problem, in this paper, we propose a framework to explore the heterogeneous decentralized markets in DeFi and NFT on the Ethereum blockchain. Based on this framework, we analyze the data of 21 exchange/lending markets in DeFi/NFT, with 184,173,656 records in total. We investigate the activity, profitability, and security of these markets. We obtain several findings to help market users through quantitative analysis. Datasets and codes are released.

[1]  K. Passi,et al.  Characterizing the OpenSea NFT Marketplace , 2022, WWW.

[2]  Lennart Ante.  Non-fungible token (NFT) markets on the Ethereum blockchain: temporal development, cointegration and interrelations , 2021, SSRN Electronic Journal.

[3]  Philipp Jovanovic,et al.  An empirical study of DeFi liquidations: incentives, risks, and instabilities , 2021, Internet Measurement Conference.

[4]  Jiahua Xu,et al.  SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols , 2021, ACM Comput. Surv..

[5]  Benjamin Livshits,et al.  On the Just-In-Time Discovery of Profit-Generating Transactions in DeFi Protocols , 2021, 2021 IEEE Symposium on Security and Privacy (SP).

[6]  Radu State,et al.  Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain , 2021, USENIX Security Symposium.

[7]  Arthur Gervais,et al.  Quantifying Blockchain Extractable Value: How dark is the forest? , 2021, 2022 IEEE Symposium on Security and Privacy (SP).

[8]  Arthur Gervais,et al.  High-Frequency Trading on Decentralized On-Chain Exchanges , 2020, 2021 IEEE Symposium on Security and Privacy (SP).

[9]  Ari Juels,et al.  Flash Boys 2.0: Frontrunning in Decentralized Exchanges, Miner Extractable Value, and Consensus Instability , 2020, 2020 IEEE Symposium on Security and Privacy (SP).

[10]  Zibin Zheng,et al.  XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO , 2020, Inf. Process. Manag..

[11]  Peilin Zheng,et al.  XBlock-ETH: Extracting and Exploring Blockchain Data From Ethereum , 2019, IEEE Open Journal of the Computer Society.

[12]  Christoph Matthies,et al.  Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems , 2019, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).

[13]  Esther H. Schor.  ARCADE , 2019, YR - The Yale Review.

[14]  Zibin Zheng,et al.  Exploiting Blockchain Data to Detect Smart Ponzi Schemes on Ethereum , 2019, IEEE Access.

[15]  Mathis Steichen,et al.  The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts , 2019, USENIX Security Symposium.

[16]  Zibin Zheng,et al.  Blockchain challenges and opportunities: a survey , 2018, Int. J. Web Grid Serv..

[17]  Yaniv Altshuler,et al.  Network Analysis of ERC20 Tokens Trading on Ethereum Blockchain , 2018 .

[18]  P. Halvorsen,et al.  Opensea , 2018, Proceedings of the 9th ACM Multimedia Systems Conference.

[19]  Xiaodong Lin,et al.  Understanding Ethereum via Graph Analysis , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[20]  Zibin Zheng,et al.  Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology , 2018, WWW.

[21]  Arvind Narayanan,et al.  BlockSci: Design and applications of a blockchain analysis platform , 2017, USENIX Security Symposium.

[22]  Yang Li,et al.  EtherQL: A Query Layer for Blockchain System , 2017, DASFAA.

[23]  Massimo Bartoletti,et al.  Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact , 2017, Future Gener. Comput. Syst..

[24]  Michael S. Kester,et al.  Bitcoin Transaction Graph Analysis , 2015, ArXiv.

[25]  Stefan Katzenbeisser,et al.  Structure and Anonymity of the Bitcoin Transaction Graph , 2013, Future Internet.

[26]  Chunhua Su,et al.  DeFiScanner: Spotting DeFi Attacks Exploiting Logic Vulnerabilities on Blockchain , 2024, IEEE Transactions on Computational Social Systems.

[27]  Wei Liang,et al.  An Investigation of Blockchain-Based Sharding , 2022, International Conference on Smart Computing and Communication.

[28]  Yinzhi Cao,et al.  An Ever-evolving Game: Evaluation of Real-world Attacks and Defenses in Ethereum Ecosystem , 2020, USENIX Security Symposium.

[29]  Jordan Tigani,et al.  Google BigQuery Analytics , 2014 .