Survey on Ethereum phishing detection technology

Zhao CAI; Tao JING; Shuang REN

doi:10.11959/j.issn.2096-109x.2023018

您当前的位置：

首页 >

文章列表页 >

Survey on Ethereum phishing detection technology

Comprehensive Reviews | 更新时间：2024-06-03

- Survey on Ethereum phishing detection technology
- Chinese Journal of Network and Information Security Vol. 9, Issue 2, Pages: 21-32(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023018
  CLC： TP393
- Online First：2023-04，
  
  Published：25 April 2023
- 稿件说明：
移动端阅览
Zhao CAI, Tao JING, Shuang REN. Survey on Ethereum phishing detection technology[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 21-32.
DOI：

Zhao CAI, Tao JING, Shuang REN. Survey on Ethereum phishing detection technology[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 21-32. DOI： 10.11959/j.issn.2096-109x.2023018.

摘要

随着区块链技术的广泛应用，网络钓鱼诈骗成为区块链平台上的一大威胁。由于区块链交易具有不可逆性、匿名性和难以篡改性等特点，网络钓鱼攻击往往具有高度的欺骗性和隐蔽性，给用户和企业带来了巨大损失。其中，以太坊平台因其智能合约功能而备受瞩目，并吸引了众多“加密货币”投资者。然而，这种广泛流行也导致了一些不法分子的涌入，滋生了许多网络犯罪行为。其中，钓鱼诈骗是以太坊平台上主要的诈骗形式之一。针对这种情况，以太坊网络钓鱼检测技术应运而生，研究者在该领域取得了众多成果，但对这些研究成果的系统分析和总结相对较少。深入分析了以太坊上网络钓鱼诈骗的现状，对已有的钓鱼诈骗检测数据集和评价指标进行了全面总结。在此基础上，进一步综述了以太坊钓鱼诈骗检测的方法，包括基于交易信息、基于图嵌入和基于图神经网络的方法。其中，基于交易信息的方法是最为常见的，通过分析交易数据的输入地址、输出地址和数额等信息，来判断交易是否存在异常。基于图嵌入和基于图神经网络的方法则更加注重对整个交易网络的分析，通过构建图结构来分析节点之间的关系，从而更加精准地识别钓鱼攻击。对比分析了各方法的优缺点，说明了各方法的适用范围和局限性。进一步指出了以太坊钓鱼诈骗检测面临的挑战，并展望了以太坊钓鱼诈骗检测未来的研究趋势。

Abstract

With the widespread application of blockchain technology

phishing scams have become a major threat to blockchain platforms.Due to the irreversibility

anonymity

and tamper-proof nature of blockchain transactions

phishing attacks often have a high degree of deception and concealment

causing significant losses to both users and businesses.Ethereum platform

with its smart contract functionality

has attracted many crypto currency investors.However

this widespread popularity has also attracted an influx of criminals

leading to the rise of cybercrime activities.Among them

phishing scams are one of the main forms of fraud on the Ethereum platform.To tackle this issue

researchers have developed Ethereum network phishing identification technology

achieving significant progress in this field.However

there has been relatively little systematic analysis and summary of these research results.The current state of phishing fraud on the Ethereum network was analyzed.Moreover

a comprehensive summary of existing phishing detection datasets and evaluation metrics were provided.On this basis

methods for detecting phishing on Ethereum were reviewed

including those based on transaction information

graph embedding and graph neural networks.Transaction information-based methods are the most common

analyzing information such as input and output addresses and amounts in transaction data to determine whether a transaction is abnormal.Methods based on graph embedding and graph neural networks place more emphasis on analyzing the entire transaction network

constructing a graph structure to analyze the relationships between nodes

and more accurately identifying phishing attacks.In addition

a comparative analysis of the advantages and disadvantages of various methods was conducted

explaining the applicability and limitations of each method.Finally

the challenges facing Ethereum phishing detection were pointed out

and the future research trends for Ethereum phishing detection were predicted.

关键词

Keywords

references

ZHENG Z , XIE S , DAI HN , et al . Blockchain challenges and opportunities:a survey [J ] . International Journal of Web and Grid Services , 2018 , 14 ( 4 ): 352 - 375 .

MONRAT A A , SCHELÉN O , ANDERSSON K . A survey of blockchain from the perspectives of applications,challenges,and opportunities [J ] . IEEE Access . 2019 , 7 : 117134 - 117151 .

YUAN Y , WANG FY . Blockchain and cryptocurrencies:Model,techniques,and applications [J ] . IEEE Transactions on Systems,Man,and Cybernetics:Systems . 2018 , 48 ( 9 ): 1421 - 1428 .

CROSBY M , PATTANAYAK P , VERMA S , et al . Blockchain technology:beyond bitcoin [J ] . Applied Innovation , 2016 , 2 ( 6-10 ): 71 .

YANG Z , YANG K , LEI L , et al . Blockchain-based decentralized trust management in vehicular networks [J ] . IEEE Internet of Things Journal , 2018 , 6 ( 2 ): 1495 - 1505 .

WANG S , OUYANG L , YUAN Y , et al . Blockchain-enabled smart contracts:architecture,applications,and future trends [J ] . IEEE Transactions on Systems,Man,and Cybernetics , 2019 , 49 ( 11 ): 2266 - 2277 .

HOLUB A , O＇CONNOR J ,, . Tracking a ukrainian bitcoin phishing ring DNS style [C ] // Proceedings of 2018 APWG Symposium on Electronic Crime Research (eCrime) . 2018 : 1 - 5 .

Ethereum under siege:scammers make $700,000 in 6 days from slack and reddit phishing attacks [EB ] .

CONTI M , KUMAR E S , LAL C , et al . A survey on security and privacy issues of bitcoin [J ] . IEEE Communications Surveys ＆ Tutorials , 2018 , 20 ( 4 ): 3416 - 3452 .

BARTOLETTI M , CARTA S , CIMOLI T , et al . Dissecting ponzi schemes on ethereum: identification, analysis, and impact [J ] . Future Generation Computer Systems , 2020 , 102 : 259 - 277 .

BUTERIN V . A next-generation smart contract and decentralized application platform [J ] . Ethereum White Paper , 2014 , 3 ( 37 ): 2 - 1 .

傅丽玉 , 陆歌皓 , 吴义明 , 等 . 区块链技术的研究及其发展综述 [J ] . 计算机科学 , 2022 , 49 ( S1 ): 447 - 461 , 666 .

FU L Y , LU G H , WU M Y , et al . A review of the research and development of blockchain technology [J ] . Computer Science , 2022 , 49 ( S1 ): 447 - 461 , 666 .

付溪 , 李晖 , 赵兴文 . 网络钓鱼识别研究综述 [J ] . 网络与信息安全学报 , 2020 , 6 ( 5 ): 1 - 10 .

FU X , LI H , ZHAO X W . Survey on phishing detection research [J ] . Chinese Journal of Network and Information Security , 2020 , 6 ( 5 ): 1 - 10 .

张茜 , 延志伟 , 李洪涛 , 等 . 网络钓鱼欺诈检测技术研究 [J ] . 网络与信息安全学报 , 2017 , 3 ( 7 ): 7 - 24 .

ZHANG X , YAN Z W , LI H T , et al . Research of phishing detection technology [J ] . Chinese Journal of Network and Information Security , 2017 , 3 ( 7 ): 7 - 24 .

ABDELHAMID N , AYESH A , THABTAH F . Phishing detection based associative classification data mining [J ] . Expert Systems with Applications , 2014 , 41 ( 13 ): 5948 - 5959 .

HAN W , CAO Y , BERTINO E , et al . Using automated individual white-list to protect web digital identities [J ] . Expert Systems with Applications , 2012 , 39 ( 15 ): 11861 - 11869 .

JAIN A K , GUPTA B B . Two-level authentication approach to protect from phishing attacks in real time [J ] . Journal of Ambient Intelligence and Humanized Computing , 2018 , 9 ( 6 ): 1783 - 1796 .

RAO R S , PAIS A R . Jail-phish:an improved search engine based phishing detection system [J ] . Computers ＆ Security , 2019 , 83 : 246 - 267 .

ZOUINA M , OUTTAJ B . A novel lightweight URL phishing detection system using SVM and similarity index [J ] . Human-centric Computing and Information Sciences , 2017 , 7 ( 1 ): 1 - 13 .

SAHINGOZ OK , BUBER E , DEMIR O , et al . Machine learning based phishing detection from URLs [J ] . Expert Systems with Applications , 2019 , 117 : 345 - 357 .

MOGHIMI M , VARJANI A Y . New rule-based phishing detection method [J ] . Expert Systems with Applications , 2016 , 53 : 231 - 242 .

Etherscan [EB ] .

Etherscamdb [EB ] .

Xblock [EB ] .

CHEN Z , WU J , XIA Y , et al . Robustness of interdependent power grids and communication networks:a complex network perspective [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2017 , 65 ( 1 ): 115 - 119 .

CHEN G , LOU Y , WANG L . A comparative study on controllability robustness of complex networks [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2019 , 66 ( 5 ): 828 - 832 .

ZHOU J , YU X , LU J A . Node importance in controlled complex networks [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2018 , 66 ( 3 ): 437 - 441 .

CHEN L , PENG J , LIU Y , et al . Phishing scams detection in ethereum transaction network [J ] . ACM Transactions on Internet Technology （TOIT） , 2020 , 21 ( 1 ): 1 - 16 .

FARRUGIA S , ELLUL J , AZZOPARDI G . Detection of illicit accounts over the Ethereum blockchain [J ] . Expert Systems with Applications , 2020 ,150:113318.

AZIZ RM , BALUCH MF , PATEL S , et al . LGBM:a machine learning approach for Ethereum fraud detection [J ] . International Journal of Information Technology , 2022 : 1 - 11 .

周健 , 闫石 , 张杰 , 等 . 基于交易特征对以太网多类型非法账户的分析与预测 [J ] . 计算机应用研究 , 2022 ( 3 ).

ZHOU J , YAN S , ZHANG J , et al . Analysis and prediction of multi-type illegal accounts of ethereum based on transaction characteristics [J ] . Application Research of Computers , 2022 ( 3 ).

周健 , 张杰 , 闫石 . 基于链上数据的区块链欺诈账户检测研究 [J ] . 计算机应用研究 , 2022 , 39 ( 4 ): 992 - 997 .

ZHOU J , ZHANG J , YAN S . Research on detection of fraudulent accounts in blockchain based on on-chain data [J ] . Application Research of Computers , 2022 , 39 ( 4 ): 992 - 997 .

IBRAHIM R F , ELIAN A M , ABABNEH M . Illicit account detection in the ethereum blockchain using machine learning [C ] // Proceedings of 2021 International Conference on Information Technology (ICIT) . 2021 : 488 - 493 .

边玲玉 , 张琳琳 , 赵楷 , 等 . 基于 LightGBM 的以太坊恶意账户检测方法 [J ] . 信息网络安全 . 2020 , 20 ( 4 ): 73 - 80 .

BIAN L Y , ZHANG L L , ZHAO K , et al . Ethereum malicious account detection method based on LightGBM [J ] . Netinfo Security , 2020 , 20 ( 4 ): 73 - 80 .

CHEN W , GUO X , CHEN Z , et al . Phishing scam detection on ethereum:towards financial security for blockchain ecosystem [C ] // Proceedings of IJCAI . 2020 : 4506 - 4512 .

POURSAFAEI F , RABBANY R , ZILIC Z . SigTran:signature Vectors for detecting illicit activities in blockchain transaction networks [C ] // Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining . 2021 : 27 - 39 .

SUN L , DOU Y , YANG C.et al . Adversarial attack and defense on graph data:a survey [J ] . arXiv preprint arXiv:1812,10528 , 2018 .

DAI H , LI H , TIAN T , et al . Adversarial attack on graph structured data [C ] // Proceedings of International Conference on Machine Learning , 2018 : 1115 - 1124 .

ZÜGNER D , AKBARNEJAD A , GÜNNEMANN S , . Adversarial attacks on neural networks for graph data [C ] // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining , 2018 : 2847 - 2856 .

ZHANG Q , FANG J , ZHANG J , et al . Cross entropy attack on deep graph infomax [C ] // Proceedings of 2020 IEEE International Symposium on Circuits and Systems (ISCAS) . 2020 : 1 - 5 .

WEN H , FANG J , WU J.et al . Transaction-based hidden strategies against general phishing detection framework on ethereum [C ] // Proceedings of 2021 IEEE International Symposium on Circuits and Systems (ISCAS) . 2021 : 1 - 5 .

PEROZZI B , AL-RFOU R ,, SKIENA S . DeepWalk:online learning of social representations [C ] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data mining , 2014 : 701 - 710 .

MIKOLOV T , SUTSKEVER I , CHEN K , et al . Distributed representations of words and phrases and their compositionality [J ] . Advances in Neural Information Processing Systems , 2013 ,26.

GROVER A , LESKOVEC J . Node2Vec:scalable feature learning for networks [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data mining . 2016 : 855 - 864 .

YUAN Q , HUANG B , ZHANG J.et al . Detecting phishing scams on ethereum based on transaction records [C ] // 2020 IEEE International Symposium on Circuits and Systems (ISCAS) . 2020 : 1 - 5 .

TANG J , QU M , WANG M.et al . Line:large-scale information network embedding [C ] // Proceedings of the 24th International Conference on World Wide Web . 2015 : 1067 - 1077 .

YUAN Z , YUAN Q , WU J . Phishing detection on ethereum via learning representation of transaction subgraphs [C ] // Proceedings of International Conference on Blockchain and Trustworthy Systems . 2020 : 178 - 191 .

NARAYANAN A , CHANDRAMOHAN M , VENKATESAN R , et al . Graph2Vec:learning distributed representations of graphs [J ] . arXiv preprint arXiv:1707.05005 , 2017 .

WU J , YUAN Q , LIN D , et al . Who are the phishers? phishing scam detection on Ethereum via network embedding [J ] . IEEE Transactions on Systems,Man,and Cybernetics:Systems , 2020 .

WANG Y , LIU Z , XU J , et al . Heterogeneous network representation learning approach for ethereum identity identification [J ] . IEEE Transactions on Computational Social Systems , 2022 .

LIN D , WU J , YUAN Q , et al . Modeling and understanding ethereum transaction records via a complex network approach [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2020 , 67 ( 11 ): 2737 - 2741 .

LIN D , WU J , YUAN Q , et al . T-edge:temporal weighted multidigraph embedding for ethereum transaction network analysis [J ] . Frontiers in Physics , 2020 ,8:204.

WANG D , CUI P , ZHU W . Structural deep network embedding [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016 : 1225 - 1234 .

CAO S , LU W , XU Q . Deep neural networks for learning graph representations [C ] // Proceedings of the AAAI Conference on Artificial Intelligence , 2016 30 ( 1 ).

WANG J , CHEN P , YU S , et al . Tsgn:transaction subgraph networks for identifying ethereum phishing accounts [C ] // Proceedings of International Conference on Blockchain and Trustworthy Systems . 2021 : 187 - 200 .

XIA Y , LIU J , WU J . Phishing detection on ethereum via attributed ego-graph embedding [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2022 , 69 ( 5 ): 2538 - 2542 .

ZHANG D , CHEN J , LU X . Blockchain phishing scam detection via multi-channel graph classification [C ] // Proceedings of International Conference on Blockchain and Trustworthy Systems . 2021 : 241 - 256 .

LI P , XIE Y , XU X , et al . Phishing fraud detection on ethereum using graph neural network [J ] . arXiv preprint arXiv:2204.08194 , 2022 .

KANEZASHI H , SUZUMURA T , LIU X , et al . Ethereum fraud detection with heterogeneous graph neural networks [J ] . arXiv preprint arXiv:2203.12363 , 2022 .

KIPF TN , WELLING M . Semi-supervised classification with graph convolutional networks [J ] . arXiv preprint arXiv:1609.02907 , 2016 .

VELIČKOVIĆ P , CUCURULL G , CASANOVA A , et al . Graph attention networks [J ] . arXiv preprint arXiv:1710.10903 , 2017 .

HAMILTON W , YING Z , LESKOVEC J . Inductive representation learning on large graphs [J ] . Advances in Neural Information Processing Systems , 2017 ,30.

Views

822

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

State-of-the-art survey of smart contract verification based on formal methods

Gate-level hardware trojan detection method based on graph Transformer

Ring adaptor signature scheme based on SM2 digital signature algorithm

Research progress on detection of software vulnerabilities based on code similarity

Survey of decentralized public key infrastructure

Related Author

Wenbo ZHANG

Simin CHEN

Lifei WEI

Wei SONG

Dongmei HUANG

Xu Yuhang

Yu Hong

Zheng Rui

Related Institution

College of Information Technology, Shanghai Ocean University

Shanghai Key Laboratory of Trustworthy Computing

Shanghai University of Electric Power

Information Engineering University

Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)

⁰