DataEther: Data Exploration Framework For Ethereum

Chen, Ting; Hu, Teng; Chen, Jiachi; Zhang, Xiaosong; Li, Zihao; Zhang, Yufei; Wang, Liu; Chen, Ang; Yang, Kun; Hu, Bin; Zhu, Tong; Deng, Shifang

doi:10.1109/icdcs.2019.00137

Cited by 44 publications

(14 citation statements)

References 28 publications

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“…Moreover, the attacker steals ETH by re-entering another function, getMyReward() in the DAO. Our observation is consistent with a recent study that getMyReward() is reentered by attackers [73]. The attacking smart contract is similar with the one re-entering splitDAO() except that it reenters getMyReward() rather than splitDAO().…”

Section: B Extending P1 To Detect Re-entrancy Attacks For Stealing Tsupporting

confidence: 91%

SODA: A Generic Online Detection Framework for Smart Contracts

Chen

Cao

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

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Smart contracts have become lucrative and profitable targets for attackers because they can hold a great amount of money. Unfortunately, existing offline approaches for discovering the vulnerabilities in smart contracts or checking the correctness of smart contracts cannot conduct online detection of attacking transactions. Besides, existing online approaches only focus on specific attacks and cannot be easily extended to detect other attacks. Moreover, developing a new online detection system for smart contracts from scratch is time-consuming and requires deep understanding of blockchain internals, thus making it difficult to quickly implement and deploy mechanisms to detect new attacks. In this paper, we propose a novel generic online detection framework named SODA for smart contracts on any blockchains that support Ethereum virtual machine (EVM). SODA distinguishes itself from existing online approaches through its capability, efficiency, and compatibility. First, SODA empowers users to easily develop apps for detecting various attacks online (i.e., when attacks happen) by separating information collection and attack detection with layered design. At the higher layer, SODA provides unified interfaces to develop detection apps against various attacks. At the lower layer, SODA instruments EVM to collect all primitive information necessary to detect various attacks and constructs 11 kinds of structural information for the ease of developing apps. Based on SODA, users can develop new apps in a few lines of code without modifying EVM. Second, SODA is efficient, because we design on-demand information retrieval to reduce the overhead of information collection and adopt dynamic linking to eliminate the overhead of inter-process communication. Such design allows users to develop detection apps using any programming languages that can generate dynamic link libraries. Third, since more and more blockchains adopt EVM as smart contract runtime, SODA can be easily migrated to such blockchains without modifying apps. Based on SODA, we develop 8 detection apps to detect the attacks exploiting major vulnerabilities in smart contracts, and integrate SODA (including all apps) into 3 popular blockchains: Ethereum, Expanse and Wanchain. The extensive experimental results demonstrate the effectiveness and efficiency of SODA and our detection apps.

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Section: B Extending P1 To Detect Re-entrancy Attacks For Stealing Tsupporting

confidence: 91%

SODA: A Generic Online Detection Framework for Smart Contracts

Chen

Cao

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

Self Cite

View full text Add to dashboard Cite

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“…Blocksci [52] constructs a platform for researchers to analyze the blockchain data. DataEther [53] is a tool to obtain the data from Ethereum, with code modification of the Ethereum clients. Google BigQuery [54] imports the data of Bitcoin and Ethereum and enables researchers to analyaze the data online while updating Ethereum data has been stopped for a long time.…”

Section: Related Work and Discussionmentioning

confidence: 99%

XBlock-ETH: Extracting and Exploring Blockchain Data From Ethereum

Zheng

et al. 2020

IEEE Open J. Comput. Soc.

128

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Blockchain-based cryptocurrencies have received extensive attention recently. Massive data has been stored on permission-less blockchains. The analysis of massive blockchain data can bring huge business values. However, the absence of well-processed up-to-date blockchain datasets impedes big data analytics of blockchain data. To fill this gap, we collect and process the up-to-date on-chain data from Ethereum, which is one of the most popular permission-less blockchains. We name such well-processed Ethereum data as XBlock-ETH, which consists of transactions, smart contracts, and cryptocurrencies (i.e., tokens). However, it is non-trivial to partition and categorize the collected raw Ethereum data to the well-processed datasets since the whole processing procedure requires sophisticated knowledge on software engineering as well as big data analytics. Moreover, we also present basic statistics and exploration for each of the well-processed datasets. Furthermore, we also outline the possible research opportunities based on XBlock-ETH, with the data and code released online.

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“…At present, the studies on blockchain data collection are mainly focused on Bitcoin and Ethereum while few of them are concentrated on EOISO. For example, DataEther [63] is a tool with code modification of the Ethereum clients to obtain the Ethereum data, while Google BigQuery [64] imports data from Bitcoin and Ethereum to enables researchers to analyze data online. XBlock-ETH [20] provides a large number of well-processed Ethereum datasets while does not include the EOSIO data.…”

Section: Related Work and Discussionmentioning

confidence: 99%

XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO

Zheng¹,

Zheng²,

Dai

et al. 2020

Preprint

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Blockchain-based cryptocurrencies and applications have flourished the blockchain research community. Massive data generated from diverse blockchain systems bring not only huge business values and but also technical challenges in data analytics of heterogeneous blockchain data. Different from Bitcoin and Ethereum, EOSIO has richer diversity and higher volume of blockchain data due to its unique architectural design in resource management, consensus scheme and high throughput. Despite its popularity (e.g., 89,800,000 blocks generated till Nov. 14, 2019 since its launching in June 8, 2018), few studies have been made on data analysis of EOSIO. To fill this gap, we collect and process the up-to-date on-chain data from EOSIO. We name these well-processed EOSIO datasets as XBlock-EOS, which consists of 7 well-processed datasets: 1) Block, Transaction and Action, 2) Internal and External EOS Transfer Action, 3) Contract Information, 4) Contract Invocation, 5) Token Action, 6) Account Creation, 7) Resource Management. It is challenging to process and analyze high volume of raw EOSIO data and establish the mapping from original raw data to the fine-grained datasets since it requires substantial efforts in exacting various types of data as well as sophisticated knowledge on software engineering and data analytics. Meanwhile, we present statistics and exploration on these datasets. Moreover, we also outline the possible research opportunities based on XBlock-EOS.

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DataEther: Data Exploration Framework For Ethereum

Cited by 44 publications

References 28 publications

SODA: A Generic Online Detection Framework for Smart Contracts

SODA: A Generic Online Detection Framework for Smart Contracts

XBlock-ETH: Extracting and Exploring Blockchain Data From Ethereum

XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO

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