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31 datasets found
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Cite
Rupak Roy/ Bob (2022). Ethereum_Fraud_Detection [Dataset]. https://www.kaggle.com/rupakroy/ethereum-fraud-detection/discussion
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Ethereum_Fraud_Detection

Create a cryptocurrency fraud detection system

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3 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jan 18, 2022
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Rupak Roy/ Bob
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

Context

This dataset contains rows of known fraud and valid transactions made over Ethereum.

Content

--Index: the index number of a row --Address: the address of the ethereum account --FLAG: whether the transaction is fraud or not --Avg min between sent tnx: Average time between sent transactions for account in minutes. --Avgminbetweenreceivedtnx: Average time between received transactions for account in minutes --TimeDiffbetweenfirstand_last(Mins): Time difference between the first and last transaction. --Sent_tnx: Total number of sent normal transactions. --Received_tnx: Total number of received normal transactions. --NumberofCreated_Contracts: Total Number of created contract transactions. --UniqueReceivedFrom_Addresses: Total Unique addresses from which account received transactions. ---UniqueSentTo_Addresses20: Total Unique addresses from which account sent transactions. --MinValueReceived: Minimum value in Ether ever received. --AvgValueReceived5Average value in Ether ever received. --MinValSent: Minimum value of Ether ever sent. --AvgValSent: Average value of Ether ever sent. --MinValueSentToContract: Minimum value of Ether sent to a contract --AvgValueSentToContract: Average value of Ether sent to contracts. --MaxValueSentToContract: Maximum value of Ether sent to a contract --TotalTransactions(IncludingTnxtoCreate_Contract): Total number of transactions --TotalEtherSent:Total Ether sent for account address --TotalEtherReceived: Total Ether received for account address --TotalEtherSent_Contracts: Total Ether sent to Contract addresses --TotalEtherBalance: Total Ether Balance following enacted transactions --TotalERC20Tnxs: Total number of ERC20 token transfer transactions --ERC20TotalEther_Received: Total ERC20 token received transactions in Ether --ERC20TotalEther_Sent: Total ERC20token sent transactions in Ether --ERC20TotalEtherSentContract: Total ERC20 token transfer to other contracts in Ether --ERC20UniqSent_Addr: Number of ERC20 token transactions sent to Unique account addresses --ERC20UniqRec_Addr: Number of ERC20 token transactions received from Unique addresses. --ERC20UniqRecContractAddr: Number of ERC20token transactions received from Unique contract addresses. --ERC20AvgTimeBetweenSent_Tnx: Average time between ERC20 token sent transactions in minutes --ERC20AvgTimeBetweenRec_Tnx: Average time between ERC20 token received transactions in minutes --ERC20AvgTimeBetweenContract_Tnx: Average time ERC20 token between sent token transactions --ERC20MinVal_Rec: Minimum value in Ether received from ERC20 token transactions for account. --ERC20MaxVal_Rec: Maximum value in Ether received from ERC20 token transactions for account --ERC20AvgVal_Rec: Average value in Ether received from ERC20 token transactions for account --ERC20MinVal_Sent: Minimum value in Ether sent from ERC20 token transactions for account --ERC20MaxVal_Sent: Maximum value in Ether sent from ERC20 token transactions for account --ERC20AvgVal_Sent: Average value in Ether sent from ERC20 token transactions for account --ERC20UniqSentTokenName: Number of Unique ERC20 tokens transferred --ERC20UniqRecTokenName: Number of Unique ERC20 tokens received --ERC20MostSentTokenType: Most sent token for account via ERC20 transaction --ERC20MostRecTokenType: Most received token for account via ERC20 transactions

Acknowledgements

The dataset acknowledges the way to perform machine learning classifiers to identify frauds in a complex network of technologies like blockchain.

Inspiration

Understanding and identifying Fraud in the blockchain space and preparing more robust fraud detection networks to fulfill the purpose of unhackble blockchain technology.

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