Demystifying Ethereum account diversity: observations, models and analysis

doi:10.1007/s11704-021-0221-3

Front. Comput. Sci.

2022, Vol. 16

Issue (4) : 164505 https://doi.org/10.1007/s11704-021-0221-3

RESEARCH ARTICLE

Demystifying Ethereum account diversity: observations, models and analysis

Chaofan WANG^1,², Xiaohai DAI^1,², Jiang XIAO^1,²(

), Chenchen LI^1,², Ming WEN^1,³, Bingbing ZHOU⁴, Hai JIN^1,²

¹. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab, Clusters and Grid Computing Lab, Wuhan 430074, China
². School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
³. School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
⁴. Centre for Distributed and High Performance Computing, School of Information Technologies, The University of Sydney, New South Wales 2006, Australia

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Abstract

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts. These massive accounts can be divided into diverse categories, such as miners, tokens, and exchanges, which is termed as account diversity in this paper. The benefit of investigating diversity are multi-fold, including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal activities. Unfortunately, the exploration of blockchain account diversity remains scarce. Even the most relevant studies, which focus on the deanonymization of the accounts on Bitcoin, can hardly be applied on Ethereum since their underlying protocols and user idioms are different. To this end, we present the first attempt to demystify the account diversity on Ethereum. The key observation is that different accounts exhibit diverse behavior patterns, leading us to propose the heuristics for classification as the premise. We then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation (MLE). We collect real-world data through extensive efforts to evaluate our proposed method and show its effectiveness. Furthermore, we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage actions. As for the former, we validate two sweeping statements reliably: (1) standalone miners are gradually replaced by the mining pools and cooperative miners; (2) transactions related to the mining pool and exchanges take up a large share of the total transactions. The latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

Keywords blockchain Ethereum classification account diversity network analysis

Corresponding Author(s): Jiang XIAO

Just Accepted Date: 26 January 2021 Issue Date: 01 December 2021

Cite this article:

Chaofan WANG,Xiaohai DAI,Jiang XIAO, et al. Demystifying Ethereum account diversity: observations, models and analysis[J]. Front. Comput. Sci., 2022, 16(4): 164505.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-021-0221-3
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I4/164505

Fig.1 An overview of our approach

Tab.1 Categories of accounts on Ethereum

Class	Feature	Parameter
Degree	Large degree	$T t = 0.01$
Degree	Balanced degree	$d i ? d o <= 2$
Value	Large value	$T l = 500 E t h$
	Small value	$T s = 1 w e i$
	Many decimals	$p d ˉ >= 8$
Interval	Fixed interval	$σ t / t ˉ <= 0.05$
	Short interval	$T w = 6$
	Intensive initiation	$n 1 / n k > 0.5$ ; $T n = 50$
Data	Data content	$d a s f = a 9059 c b b$
	Data size	$T i = 128$
	Similarity	$l d < 0.05$

Tab.2 Detailed parameters taken to quantify features

Fig.2 Simple case of MLE-based classification. (a) Heuristic Results; (b) Probability matrices; (c) MLE Results

Fig.3 Normal case of MLE-based classification. (a) Insufficient information; (b) Sufficient information

Fig.4 Two stages of heuristics

Fig.5 Validation results with different methods. (a) Coverage rate; (b) Accuracy rate; (c) F1 score

Fig.6 Variance of accounts and transactions over time

Fig.7 Change of ether price and mining difficulty

Fig.8 Number change of different categories over time. (a) With co-miners; (b) Without co-miners

Fig.9 Ratio change of transactions related to different categories over time

Fig.10 Sketch map of arbitrage activities

Fig.11 Disguise actions. (a) Multi-hop pattern; (b) Splitting pattern

Fig.12 Arbitrage actions among different exchanges

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