skip to main content
research-article

Phishing Scams Detection in Ethereum Transaction Network

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
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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

View all
  • (2025)Early Detection of Malicious Crypto Addresses With Asset Path Tracing and SelectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352277237:3(1154-1166)Online publication date: Mar-2025
  • (2025)SpaTeD: Sparsity-Aware Tensor Decomposition-Based Representation Learning Framework for Phishing Scams DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.346255212:1(320-334)Online publication date: Feb-2025
  • (2025)TSFF: A Triple-Stream Feature Fusion Method for Ethereum Phishing Scam DetectionIEEE Internet of Things Journal10.1109/JIOT.2024.347377112:3(2623-2632)Online publication date: 1-Feb-2025
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

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
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

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

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Cryptocurrency
  2. graph convolutional network
  3. graph embedding
  4. node classification
  5. phishing detection

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

  • 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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)502
  • Downloads (Last 6 weeks)42
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Early Detection of Malicious Crypto Addresses With Asset Path Tracing and SelectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352277237:3(1154-1166)Online publication date: Mar-2025
  • (2025)SpaTeD: Sparsity-Aware Tensor Decomposition-Based Representation Learning Framework for Phishing Scams DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.346255212:1(320-334)Online publication date: Feb-2025
  • (2025)TSFF: A Triple-Stream Feature Fusion Method for Ethereum Phishing Scam DetectionIEEE Internet of Things Journal10.1109/JIOT.2024.347377112:3(2623-2632)Online publication date: 1-Feb-2025
  • (2024)EtherWatch: A Framework for Detecting Suspicious Ethereum Accounts and Their ActivitiesJournal of Information Processing10.2197/ipsjjip.32.78932(789-800)Online publication date: 2024
  • (2024)Provably powerful graph neural networks for directed multigraphsProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i10.29069(11838-11846)Online publication date: 20-Feb-2024
  • (2024)Phishing behavior detection on different blockchains via adversarial domain adaptationCybersecurity10.1186/s42400-024-00237-57:1Online publication date: 19-Jun-2024
  • (2024)TPGraph: A Highly-scalable Time-partitioned Graph Model for Tracing BlockchainProceedings of the 17th ACM International Systems and Storage Conference10.1145/3688351.3689161(25-38)Online publication date: 16-Sep-2024
  • (2024)FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud DetectionProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698648(292-300)Online publication date: 14-Nov-2024
  • (2024)Cross-chain Abnormal Transaction Detection via Graph-based Multi-model FusionProceedings of the 6th ACM International Symposium on Blockchain and Secure Critical Infrastructure10.1145/3659463.3660008(1-9)Online publication date: 2-Jul-2024
  • (2024)Artificial Intelligence for Web 3.0: A Comprehensive SurveyACM Computing Surveys10.1145/365728456:10(1-39)Online publication date: 14-May-2024
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media