research-article

Phishing Scams Detection in Ethereum Transaction Network

Authors:

Fenfang Xie, and

Zibin ZhengAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 1

Article No.: 10, Pages 1 - 16

https://doi.org/10.1145/3398071

Published: 17 December 2020 Publication History

Abstract

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.

References

[1]

Shuichiro Haruta, Hiromu Asahina, and Iwao Sasase. 2017. Visual similarity-based phishing detection scheme using image and CSS with target Website finder. In 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, December 4-8, 2017. IEEE, 1--6. https://doi.org/10.1109/GLOCOM.2017.8254506

Digital Library

[2]

Neda Abdelhamid, Aladdin Ayesh, and Fadi A. Thabtah. 2014. Phishing detection-based associative classification data mining. Expert Syst. Appl. 41, 13 (2014), 5948--5959.

[3]

Amr Ahmed, Nino Shervashidze, Shravan M. Narayanamurthy, Vanja Josifovski, and Alexander J. Smola. 2013. Distributed large-scale natural graph factorization. In Proceedings of the 22nd International World Wide Web Conference (WWW’13). 37--48.

[4]

Moses Adebowale Akanbi, Khin T. Lwin, E. Sánchez, and M. Alamgir Hossain. 2019. Intelligent web-phishing detection and protection scheme using integrated features of images, frames and text. Expert Syst. Appl. 115 (2019), 300--313.

[5]

Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proceedings of the Conference on Advances in Neural Information Processing Systems: Natural and Synthetic (NIPS’01). 585--591.

Digital Library

[6]

M. Bellingeri, D. Bevacqua, F. Scotognella, and D. Cassi. 2019. The heterogeneity in link weights may decrease the robustness of real-world complex weighted networks. Sci. Rep. 9, 1 (2019), 1--13.

[7]

Stefano Boccaletti, Vito Latora, Yamir Moreno, Martin Chavez, and D.-U. Hwang. 2006. Complex networks: Structure and dynamics. Phys. Rep. 424, 4-5 (2006), 175--308.

[8]

Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. GraRep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, (CIKM’15). 891--900.

Digital Library

[9]

Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 1145--1152.

[10]

Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka, and Tom M. Mitchell. 2010. Toward an architecture for never-ending language learning. In Proceedings of the 24th AAAI Conference on Artificial Intelligence.

[11]

Weili Chen, Jun Wu, Zibin Zheng, Chuan Chen, and Yuren Zhou. 2019. Market manipulation of bitcoin: Evidence from mining the Mt. Gox transaction network. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’19). 964--972.

Digital Library

[12]

Weili Chen, Zibin Zheng, Edith Cheuk-Han Ngai, Peilin Zheng, and Yuren Zhou. 2019. Exploiting blockchain data to detect smart ponzi schemes on ethereum. IEEE Access 7 (2019), 37575--37586.

[13]

Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. 2019. Deep anomaly detection on attributed networks. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 594--602.

[14]

Yong Fang, Cheng Zhang, Cheng Huang, Liang Liu, and Yue Yang. 2019. Phishing email detection using improved RCNN model with multilevel vectors and attention mechanism. IEEE Access 7 (2019), 56329--56340.

[15]

Palash Goyal and Emilio Ferrara. 2018. Graph embedding techniques, applications, and performance: A survey. Knowl.-Based Syst. 151 (2018), 78--94.

[16]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855--864.

Digital Library

[17]

John Guare. 1990. Six Degrees of Separation: A Play. Vintage.

[18]

Yuexian Hou, Peng Zhang, Xingxing Xu, Xiaowei Zhang, and Wenjie Li. 2009. Nonlinear dimensionality reduction by locally linear inlaying. IEEE Trans. Neural Netw. 20, 2 (2009), 300--315.

Digital Library

[19]

Ankit Kumar Jain and B. B. Gupta. 2018. Towards detection of phishing websites on client-side using machine learning-based approach. Telecommun. Syst. 68, 4 (01 Aug 2018), 687--700.

[20]

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems. MIT Press, 3146--3154.

[21]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. Retrieved from https://arXiv:1412.6980.

[22]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. Retrieved from https://arXiv:1609.02907.

[23]

Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. Retrieved from https://arxiv:1611.07308.

[24]

Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi (Eds.). 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.

Digital Library

[25]

Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. Ann. Math. Stat. 22, 1 (1951), 79--86.

[26]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444.

[27]

Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18), the 30th Innovative Applications of Artificial Intelligence (IAAI’18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI’18). 3538--3545.

[28]

Yukun Li, Zhenguo Yang, Xu Chen, Huaping Yuan, and Wenyin Liu. 2019. A stacking model using URL and HTML features for phishing webpage detection. Future Gen. Comput. Syst. 94 (2019), 27--39.

Digital Library

[29]

Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. 2018. Heterogeneous graph neural networks for malicious account detection. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18). 2077--2085.

Digital Library

[30]

Jian Mao, Jingdong Bian, Wenqian Tian, Shishi Zhu, Tao Wei, Aili Li, and Zhenkai Liang. 2019. Phishing page detection via learning classifiers from page layout feature. EURASIP J. Wireless Comm. Netw. 2019 (2019), 43.

[31]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. Retrieved from https://arXiv:1301.3781.

[32]

Satoshi Nakamoto and A. Bitcoin. 2008. A peer-to-peer electronic cash system. Bitcoin. https://bitcoin.org/bitcoin.pdf4.

[33]

Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1105--1114.

Digital Library

[34]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 701--710.

Digital Library

[35]

Marc Pilkington. 2016. Blockchain technology: principles and applications. In Research Handbook on Digital Transformations. Edward Elgar Publishing.

[36]

Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018. DeepInf: Social influence prediction with deep learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’18). 2110--2119.

Digital Library

[37]

Routhu Srinivasa Rao and Alwyn Roshan Pais. 2019. Jail-Phish: An improved search engine-based phishing detection system. Comput. Secur. 83 (2019), 246--267.

Digital Library

[38]

Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. The truly deep graph convolutional networks for node classification. Retrieved from https://arxiv:1907.10903.

[39]

Ozgur Koray Sahingoz, Ebubekir Buber, Önder Demir, and Banu Diri. 2019. Machine learning-based phishing detection from URLs. Expert Syst. Appl. 117 (2019), 345--357.

[40]

Nick Szabo. 1997. Formalizing and securing relationships on public networks. First Monday 2, 9 (1997).

[41]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). 1067--1077.

Digital Library

[42]

Duncan J. Watts and Steven H. Strogatz. 1998. Collective dynamics of ‘small-world’ networks. Nature 393, 6684 (1998), 440.

[43]

Emma J. Williams and Danielle Polage. 2019. How persuasive is phishing email? The role of authentic design, influence and current events in email judgements. Behav. IT 38, 2 (2019), 184--197.

[44]

Jiajing Wu, Qi Yuan, Dan Lin, Wei You, Weili Chen, Chuan Chen, and Zibin Zheng. 2019. Who are the phishers? Phishing scam detection on ethereum via network embedding. Retrieved from https://arXiv:1911.09259.

[45]

Guanhua Yan, Stephan Eidenbenz, and Emanuele Galli. 2009. SMS-watchdog: Profiling social behaviors of SMS users for anomaly detection. In Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection (RAID’09). 202--223.

Digital Library

[46]

Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning-based natural language processing. IEEE Comput. Intell. Mag. 13, 3 (2018), 55--75.

[47]

Y. Yuan and F. Wang. 2018. Blockchain and cryptocurrencies: Model, techniques, and applications. IEEE Trans. Syst. Man Cybernet.: Syst. 48, 9 (Sep. 2018), 1421--1428.

[48]

Xi Zhang, Zhiwei Yan, Hongtao Li, and Guanggang Geng. 2017. Chinese journal of network and information security. Res. Phish. Detect. Technol. 7 (2017).

[49]

Zibin Zheng, Shaoan Xie, Hong-Ning Dai, Xiangping Chen, and Huaimin Wang. 2018. Blockchain challenges and opportunities: A survey. Int. J. Web Grid Services 14, 4 (2018), 352--375.

Cited By

Yan CHan XZhu YDu DLu ZLiu Y(2024)Phishing behavior detection on different blockchains via adversarial domain adaptationCybersecurity10.1186/s42400-024-00237-57:1Online publication date: 19-Jun-2024
https://doi.org/10.1186/s42400-024-00237-5
Shen MTan ZNiyato DLiu YKang JXiong ZZhu LWang WShen X(2024)Artificial Intelligence for Web 3.0: A Comprehensive SurveyACM Computing Surveys10.1145/365728456:10(1-39)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3657284
Li WBao LChen JGrundy JXia XYang X(2024)Market Manipulation of Cryptocurrencies: Evidence from Social Media and Transaction DataACM Transactions on Internet Technology10.1145/364381224:2(1-26)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3643812
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Index Terms

Phishing Scams Detection in Ethereum Transaction Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 1

Visions Paper, Regular Papers, SI: Blockchain in E-Commerce, and SI: Human-Centered Security, Privacy, and Trust in the Internet of Things

February 2021

534 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3441681

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 December 2020

Online AM: 07 May 2020

Accepted: 01 May 2020

Revised: 01 February 2020

Received: 01 October 2019

Published in TOIT Volume 21, Issue 1

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National Natural Science Foundation of China
Program for Guangdong Introducing Innovative and Entrepreneurial Teams
Key Research and Development Program of Guangdong Province of China
Guangdong Basic and Applied Basic Research Foundation

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Cited By

Yan CHan XZhu YDu DLu ZLiu Y(2024)Phishing behavior detection on different blockchains via adversarial domain adaptationCybersecurity10.1186/s42400-024-00237-57:1Online publication date: 19-Jun-2024
https://doi.org/10.1186/s42400-024-00237-5
Shen MTan ZNiyato DLiu YKang JXiong ZZhu LWang WShen X(2024)Artificial Intelligence for Web 3.0: A Comprehensive SurveyACM Computing Surveys10.1145/365728456:10(1-39)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3657284
Li WBao LChen JGrundy JXia XYang X(2024)Market Manipulation of Cryptocurrencies: Evidence from Social Media and Transaction DataACM Transactions on Internet Technology10.1145/364381224:2(1-26)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3643812
Pourmohamad RWirsz SOest ABao TShoshitaishvili YWang RDoupé ABazzi RQuek TGao DZhou JCardenas A(2024)Deep Dive into Client-Side Anti-Phishing: A Longitudinal Study Bridging Academia and IndustryProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3657027(638-653)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3657027
Lin DWu JHuang TLin KZheng Z(2024)Who is Who on Ethereum? Account Labeling Using Heterophilic Graph Convolutional NetworkIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.332952054:3(1541-1553)Online publication date: Mar-2024
https://doi.org/10.1109/TSMC.2023.3329520
Qi YWu JXu HGuizani M(2024)Blockchain Data Mining With Graph Learning: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.332740446:2(729-748)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TPAMI.2023.3327404
Sun HLiu ZWang SWang H(2024)Adaptive Attention-Based Graph Representation Learning to Detect Phishing Accounts on the Ethereum BlockchainIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.335508911:3(2963-2975)Online publication date: May-2024
https://doi.org/10.1109/TNSE.2024.3355089
Voronov TRaz DRottenstreich O(2024)A Framework for Anomaly Detection in Blockchain Networks With SketchesIEEE/ACM Transactions on Networking10.1109/TNET.2023.329825332:1(686-698)Online publication date: Feb-2024
https://doi.org/10.1109/TNET.2023.3298253
Huang HZhang XWang JGao CLi XZhu RMa Q(2024)PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.334907111:3(4326-4339)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3349071
Krishnan LVakilinia IReddivari SAhuja S(2024)Handling Imbalanced Data for Detecting Scams in Ethereum Transactions Using Sampling Techniques2024 12th International Symposium on Digital Forensics and Security (ISDFS)10.1109/ISDFS60797.2024.10527318(1-6)Online publication date: 29-Apr-2024
https://doi.org/10.1109/ISDFS60797.2024.10527318
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