区块链数据集（一）Xblock
一、Transaction Datasets
Ethereum On-chain Data
[Dataset] 2021-10TransactionData/Code AvailableEthereum
Introduction: This is the dataset of paper “XBlock-ETH: Extracting and Exploring Blockchain Data From Ethereum”.
Data / Code Paper CiteDownloads: 4223
Ethereum Phishing Transaction Network
[Dataset] 2021-10Anomaly DetectionTransactionData/Code AvailableEthereum
Introduction: This is the dataset of paper “Phishing Scams Detection in Ethereum Transaction Network”. （GCN+AE）
Data / Code Paper CiteDownloads: 2608
压缩包：QmdMVccE2ymMyiRmyxVQSVm3JUNh18k7XKSbwQ6JsiPSBx
First-order Transaction Network of Phishing Nodes
[Dataset] 2021-10Anomaly DetectionTransactionData/Code AvailableEthereum
Introduction: This is the code and dataset related to paper “Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding”.（trans2vec）
Data / Code Paper CiteDownloads: 1680
压缩包：QmZSVmw9wh19PpHsHEmGnbmCUmcPfoSbB8syqsapFhrq6y.zip
Second-order Transaction Network of Phishing Nodes
[Dataset] 2021-10Anomaly DetectionTransactionData/Code AvailableEthereum
Introduction: This is the dataset of paper “Phishing Detection on Ethereum via Learning Representation of Transaction Subgraphs”.
Data / CodeDownloads: 1115
Bitcoin Partial Transaction Dataset
[Dataset] 2021-10BitcoinTransactionData/Code Available
Introduction: This is the dataset of paper “Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs”.
Data / Code Paper CiteDownloads: 993
EOSIO On-chain Data
[Dataset] 2021-10TransactionEOSData/Code Available
Introduction: This is the dataset of paper “XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO”.
Data / Code Paper CiteDownloads: 988
没有数据集
Ethereum Partial Transaction Dataset
[Dataset] 2021-10TransactionData/Code AvailableEthereum
Introduction: This is the dataset of paper “Modeling and Understanding {Ethereum} Transaction Records via A Complex Network Approach”.
Data / Code Paper CiteDownloads: 523
Transactions of Cryptocurrency Exchange Accounts
[Dataset] 2022-5CryptocurrencyTransactionMarketInPlusLabEthereum
Introduction: This is the dataset of paper “On-chain analysis-based detection of abnormal transaction amount on cryptocurrency exchanges”.
Data / Code Paper CiteDownloads: 257
EOSIO On-chain Data(transactions information)
[Dataset] 2023-10CryptocurrencyTransaction RelationshipsTransaction
Introduction: Our work run a EOSIO to get the on-chain data about transactions information from EOSIO and process them into the following datasets.
Data / CodeDownloads: 59
#002 Contract Datasets
Smart Contract Attribute Dataset
[Dataset] 2021-10Smart ContractData/Code Available
Introduction: This is the dataset of paper “Recommending differentiated code to support smart contract update”.
Data / Code Paper CiteDownloads: 881
Ponzi Contract Dataset
[Dataset] 2022-11Anomaly DetectionSmart ContractEthereum
Introduction: This is the dataset of paper “Securing the Ethereum from Smart Ponzi Schemes: Identification Using Static Features”.
Data / CodeDownloads: 627
#003 Market Datasets
Mt.Gox Leaked Transaction
[Dataset] 2021-10BitcoinMarketData/Code Available
Introduction: This is the dataset of paper “Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network”.
Data / Code Paper CiteDownloads: 434
Bitcoin Price and Volume Dataset
[Dataset] 2021-9BitcoinMarketData/Code Available
Introduction: This is the dataset of paper “Long-range dependence, multi-fractality and volume-return causality of Ether market”.
Data / CodeDownloads: 386
Ether Price and Volume Dataset
[Dataset] 2021-9MarketData/Code AvailableEthereum
Introduction: This is the dataset of paper “Long-range dependence, multi-fractality and volume-return causality of Ether market”.
Data / CodeDownloads: 320
Activity Information of DApps
[Dataset] 2021-10MarketDAppData/Code Available
Introduction: This is the dataset of paper “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends”.
Data / Code Paper CiteDownloads: 264
Transactions of Cryptocurrency Exchange Accounts
[Dataset] 2022-5CryptocurrencyTransactionMarketInPlusLabEthereum
Introduction: This is the dataset of paper “On-chain analysis-based detection of abnormal transaction amount on cryptocurrency exchanges”.
Data / Code Paper CiteDownloads: 259
Ethereum DeFi and NFT Market Data
[Dataset] 2023-1CryptocurrencyMarketData/Code AvailableEthereum
Introduction: This is the dataset of paper “Exploring Heterogeneous Decentralized Markets in DeFi and NFT on Ethereum Blockchain”.
Data / Code
#004 Anomaly Detection
Phishing Scams Detection in Ethereum Transaction Network
[Paper] 2020-9Anomaly DetectionInPlusLabData/Code AvailableEthereum
Introduction: Blockchain has attracted an increasing amount of researches, and there are lots of refreshing implementations in different fields. Cryptocurrency as its representative implementation, suffers the economic loss due to phishing scams. In our work, accounts and transactions are treated as nodes and edges, thus detection of phishing accounts can be modeled as a node classification problem. Correspondingly, we propose a detecting method based on Graph Convolutional Network and autoencoder to precisely distinguish phishing accounts. Experiments on different large-scale real-world datasets from Ethereum show that our proposed model consistently performs promising results compared with related methods.
PDF Dataset PPT CiteDownloads: 753
压缩包：QmdMVccE2ymMyiRmyxVQSVm3JUNh18k7XKSbwQ6JsiPSBx
Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding
[Paper] 2020-9Anomaly DetectionInPlusLabData/Code AvailableEthereum
Introduction: Recently, blockchain technology has become a topic in the spotlight but also a hotbed of various cybercrimes. Among them, phishing scams on blockchain have been found to make a notable amount of money, thus emerging as a serious threat to the trading security of the blockchain ecosystem. In order to create a favorable environment for investment, an effective method for detecting phishing scams is urgently needed in the blockchain ecosystem. To this end, this article proposes an approach to detect phishing scams on Ethereum by mining its transaction records. Specifically, we first crawl the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records. Then, by taking the transaction amount and timestamp into consideration, we propose a novel network embedding algorithm called trans2vec to extract the features of the addresses for subsequent phishing identification. Finally, we adopt the one-class support vector machine (SVM) to classify the nodes into normal and phishing ones. Experimental results demonstrate that the phishing detection method works effectively on Ethereum, and indicate the efficacy of trans2vec over existing state-of-the-art algorithms on feature extraction for transaction networks. This work is the first investigation on phishing detection on Ethereum via network embedding and provides insights into how features of large-scale transaction networks can be embedded.
PDF Dataset PPT CiteDownloads: 461
压缩包：QmZSVmw9wh19PpHsHEmGnbmCUmcPfoSbB8syqsapFhrq6y.zip
Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem
[Paper] 2022-1Anomaly DetectionInPlusLabData/Code AvailableEthereum
Introduction: In recent years, blockchain technology has created a new cryptocurrency world and has attracted a lot of attention. It also is rampant with various scams. For example, phishing scams have grabbed a lot of money and have become an important threat to users’ financial security in the blockchain ecosystem. To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness. Specifically, we propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Extensive experiments show that the proposed algorithm can effectively identify phishing scams.
PDF Dataset PPT CiteDownloads: 367
On-chain analysis-based detection of abnormal transaction amount on cryptocurrency exchanges
[Paper] 2022-10CryptocurrencyAnomaly DetectionInPlusLabData/Code Available
Introduction: Cryptocurrency exchanges play an indispensable role in the cryptocurrency market. However, some exchanges are suspected to be involved in various abnormal or malicious behaviors while providing services to users, such as money laundering, wash trading and even running away. Besides, these behaviors are reported to be often accompanied by an anomalous increase in the transaction amount. Therefore, it is a topic worthy of study to detect whether the abnormal transaction amount occurs in the exchange and when it occurs. This paper uses web crawler tools to collect a relatively complete dataset of exchanges and then conducts a correlation analysis to obtain the most important factors that influence the transaction amount of different exchanges. Then, the prediction model of the influence of various factors on the transaction amount is obtained based on deep learning. The deviation between the predicting transaction amount and the actual transaction amount is calculated to provide a basis for abnormal transaction amount detection. Finally, through a case study on the detection results, some abnormal transaction amounts are related to policy changes and industry events, while the others are suspected to be related to illegal behaviors.
PDF Dataset PPT CiteDownloads: 201
Detecting Mixing Services via Mining Bitcoin Transaction Network With Hybrid Motifs
[Paper] 2021-1BitcoinAnomaly DetectionInPlusLabData/Code Available
Introduction: As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly, mixing services in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.
PDF Dataset PPT CiteDownloads: 185
Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology
[Paper] 2018-4Anomaly DetectionInPlusLabEthereum
Introduction: Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
PDF Dataset PPT Cite
