Phishing Detection on Ethereum via Learning Representation of Transaction Subgraphs

Yuan, Zihao; Yuan, Qi; Wu, Jiajing

doi:10.1007/978-981-15-9213-3_14

Cited by 51 publications

(33 citation statements)

References 17 publications

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“…Wu et al [19] redesigned the walking strategy by using transaction volume, timestamps, and multiedges features to make their embedding framework more suitable for this task. Subsequently, Yuan et al [15] extracted the subgraphs for each target account and embedded their transaction topology into feature vector via an embedding method named Graph2Vec [25]. Besides, they introduced the line graph [26] to further enhance the network structure embedding.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, the emergence of related research has helped to analyze the transaction pattern and account behavior on the blockchain system, and most of them leverage graph modeling methods. Such as evolution analysis of market via the on-chain transaction graph [8][9][10][11][12], transaction patterns recognition via graph topology and motifs [13,14], detection of abnormal users or transactions via graph embedding or graph neural network [15,16], etc. Among them, identity inference, which can be regarded as a de-anonymization process, is particularly important in blockchain data mining.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the subgraph perspective [15], we propose a framework to reconcile the scalability and end-to-end solution for identity inference. Benefiting from previous work, we collect two kinds of on-chain transaction data including Ethereum and EOSIO, to infer the "phisher" and "bot" accounts, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identity Inference on Blockchain using Graph Neural Network

Shen,

Zhou,

Xie

et al. 2021

Preprint

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The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently call for effective regulation to detect these illegal behaviors to ensure the safety and stability of user transactions. Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. As a common tool, graph mining technique can effectively represent the interactive information between accounts and be used for identity inference. However, existing methods cannot balance scalability and end-to-end architecture, resulting high computational consumption and weak feature representation. In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in largescale graph. Furthermore, we propose a generic end-to-end graph neural network model, named I 2 BGNN, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity, achieving de-anonymization. Extensive experiments on EOSG and ETHG datasets demonstrate that the proposed method achieve the state-of-the-art performance in identity inference.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identity Inference on Blockchain using Graph Neural Network

Shen,

Zhou,

Xie

et al. 2021

Preprint

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“…One can see that these methods mentioned above mainly built phishing account detection as a node classification task, which can not capture more potential global structural features for phishing accounts. Yuan et al [24] built phishing identification problem as the graph classification task, which used line graph to enhance the Graph2Vec method and achieved good performance. However, Yuan et al only consider the structural features obtained from line graphs, ignoring the direction information, which plays a significant role in phishing scams' identification problem.…”

Section: Phishing Identificationmentioning

confidence: 99%

TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts

Wang¹,

Chen²,

Yu³

et al. 2021

Preprint

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Blockchain technology and, in particular, blockchain-based transaction offers us information that has never been seen before in the financial world. In contrast to fiat currencies, transactions through virtual currencies like Bitcoin are completely public. And these transactions of cryptocurrencies are permanently recorded on Blockchain and are available at any time. Therefore, this allows us to build transaction networks (TN) to analyze illegal phenomenons such as phishing scams in blockchain from a network perspective. In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum. Firstly we extract transaction subgraphs for each address and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts. Moreover, Directed-TSGNs, by introducing direction attributes, can retain the transaction flow information that captures the significant topological pattern of phishing scams. By comparing with the TSGN, Directed-TSGN indeed has much lower time complexity, benefiting the graph representation learning. Experimental results demonstrate that, combined with network representation algorithms, the TSGN model can capture more features to enhance the classification algorithm and improve phishing nodes' identification accuracy in the Ethereum networks.

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“…However, along with the rapid development of blockchain technology, various types of cybercrimes have arisen endlessly and thus Ethereum has become a hotbed of various cybercrimes [3,4,5]. Due to the anonymity of the blockchain, criminals attempt to evade supervision and engage in illegal activities by injecting funds into the blockchain system.…”

Section: Introductionmentioning

confidence: 99%

Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking

Xie¹,

Jin²,

Zhang³

et al. 2021

Preprint

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With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain. In order to better understand this emerging market and explore more efficient countermeasures for effective supervision, it is imperative to track transactions on blockchain-based systems. Due to the openness of Ethereum, we can easily access the publicly available transaction records, model them as a complex network, and further study the problem of transaction tracking via link prediction, which provides a deeper understanding of Ethereum transactions from a network perspective. Specifically, we introduce an embedding based link prediction framework that is composed of temporal-amount snapshot multigraph (TASMG) and present temporal-amount walk (TAW). By taking the realistic rules and features of transaction networks into consideration, we propose TASMG to model Ethereum transaction records as a temporal-amount network and then present TAW to effectively embed accounts via their transaction records, which integrates temporal and amount information of the proposed network. Experimental results demonstrate the superiority of the proposed framework in learning more informative representations and could be an effective method for transaction tracking.

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Phishing Detection on Ethereum via Learning Representation of Transaction Subgraphs

Cited by 51 publications

References 17 publications

Identity Inference on Blockchain using Graph Neural Network

Identity Inference on Blockchain using Graph Neural Network

TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts

Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking

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