skip to main content
research-article

Analyzing Malicious Activities and Detecting Adversarial Behavior in Cryptocurrency based Permissionless Blockchains: An Ethereum Usecase

Published: 10 December 2022 Publication History

Abstract

Different malicious activities occur in cryptocurrency-based permissionless blockchains such as Ethereum and Bitcoin. Some activities are due to the exploitation of vulnerabilities which are present in the blockchain infrastructure, some activities target its users through social engineering techniques, while some activities use it to facilitate different malicious activities. Since cryptocurrency-based permissionless blockchains provide pseudonymity to its users, bad actors prefer to carry out transactions related to malicious activities on them. Towards this, we aim at automatically flagging blockchain accounts as suspects that indulge in malicious activities, thus reducing the unintended support that cryptocurrency-based permissionless blockchains provide to malicious actors. We first use the cosine similarity (CS) metrics to study the similarities between the feature vector of accounts associated with different malicious activities and find that most of the malicious activities associated with the Ethereum blockchain behave similarly. We then use the K-Means clustering algorithm to check if accounts associated with similar malicious activities cluster together. We also study the effect of bias on the performance of a machine learning (ML) algorithm, due to the number of accounts associated with malicious activity. We then compare the different state-of-the-art models and identify that Neural Networks (NNs) are resistant to bias associated with a malicious activity and are also robust against adversarial attacks. The previously used ML algorithms for identifying malicious accounts also show bias towards an over-represented malicious activity.

Acknowledgment

We thank the authors of [1] for sharing with us the account hashes of all the 2,946 malicious accounts until 7th December 2019, 680,314 benign accounts, and 1,736 malicious accounts until 27th May 2020.

References

[1]
R. Agarwal, S. Barve, and S. Shukla. 2021. Detecting malicious accounts in permissionless blockchains using temporal graph properties. Applied Network Science 6, 9 (2021), 1–30.
[2]
I. Alarab, S. Prakoonwit, and M. Nacer. 2020. Competence of graph convolutional networks for anti-money laundering in bitcoin blockchain. In Proceedings of the 5th International Conference on Machine Learning Technologies. ACM, Beijing, China, 23–27.
[3]
A. Alkhalifah, A. Ng, A. Kayes, J. Chowdhury, M. Alazab, and P. Watters. 2020. A taxonomy of blockchain threats and vulnerabilities. In Proceedings of the Blockchain for Cybersecurity and Privacy. Y. Maleh, M. Shojafar, M. Alazab, and I. Romdhani (Eds.), CRC,3–28.
[4]
M. Bartoletti, B. Pes, and S. Serusi. 2018. Data mining for detecting bitcoin ponzi schemes. In Proceedings of the Crypto Valley Conference on Blockchain Technology. IEEE, Zug, 75–84.
[5]
Behind MLM. 2019. Plus Token Ponzi Collapses, Chinese Media Report $2.9 Billion in Losses. Retrieved October 4, 2020 from https://bit.ly/2SuLuSI.
[6]
V. Buterin. 2013. Ethereum: A Next-Generation SmartContract and Decentralized Application Platform. Retrieved July 30, 2020 from https://bit.ly/3ibUzOj.
[7]
R. Camino, C. F. Torres, M. Baden, and R. State. 2020. A data science approach for detecting honeypots in ethereum. In Proceedings of the International Conference on Blockchain and Cryptocurrency. IEEE, Toronto, Canada, 1–9.
[8]
D. Chaudhari, R. Agarwal, and S. Shukla. 2021. Towards malicious account identification in Bitcoin. In Proceedings of the IEEE Blockchain Workshop on Blockchain Security, Application, and Performance. IEEE, Melbourne, 1–8.
[9]
H. Chen and L. Jiang. 2019. Efficient GAN-based method for cyber-intrusion detection. arXiv:1904.02426. Retrieved from https://arxiv.org/abs/1904.02426
[10]
H. Chen, M. Pendleton, L. Njilla, and S. Xu. 2020. A survey on ethereum systems security: Vulnerabilities, attacks, and defenses. ACM Computing Surveys 53, 3 (2020), 1–43.
[11]
W. Chen, Z. Zheng, J. Cui, E. Ngai, P. Zheng, and Y. Zhou. 2018. Detecting ponzi schemes on ethereum: Towards healthier blockchain technology. In Proceedings of the World Wide Web Conference. International World Wide Web Conferences Steering Committee, Lyon, 1409–1418.
[12]
Z. Cheng, X. Hou, R. Li, Y. Zhou, X. Luo, J. Li, and K. Ren. 2019. Towards a first step to understand the cryptocurrency stealing attack on ethereum. In Proceedings of the 22nd International Symposium on Research in Attacks, Intrusions, and Defenses. USENIX Association, Beijing, 47–60.
[13]
M. De Choudhury. 2011. Tie formation on twitter: Homophily and structure of egocentric networks. In Proceedings of the 3rd International Conference on Privacy, Security, Risk, and Trust and 3rd International Conference on Social Computing. IEEE, Boston, 465–470.
[14]
A. Emari, Salam, Anbar Mohammed, Sanjalawe, Yousef, Manickam, and Selvakumar. 2021. A Labeled Transactions-Based Dataset on the Ethereum Network. Springer, Penang, Malaysia, 61–79. DOI:
[15]
Etherscan. 2020. Ethereum Developer APIs. Retrieved October 9, 2020 from https://bit.ly/3fKYBM3.
[16]
Etherscan. 2020. Label Word Cloud. Retrieved October 9, 2020 from https://bit.ly/3g48B1R.
[17]
A. Goharshady. 2021. Irrationality, extortion, or trusted third-parties: Why it is impossible to buy and sell physical goods securely on the blockchain. In Proceedings of the IEEE International Conference on Blockchain. IEEE, Melbourne, 73–81.
[18]
I. Goodfellow, J. Abadie, M. Mirza, B. Xu, D. Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems. NeurIPS, Montreal, Canada, 2672–2680.
[19]
Hacken. 2019. How to Avoid a Hack: Cryptopia “Success” Case. Retrieved October 4, 2020 from https://bit.ly/3uQsTRv.
[20]
M. Huillet. 2019. Upbit Hack: Stolen ETH Worth Millions on the Move to Unknown Wallets. Retrieved October 4, 2020 from https://bit.ly/3ixgeNp.
[21]
N. Kumar, A. Singh, A. Handa, and S. Shukla. 2020. Detecting malicious accounts on the ethereum blockchain with supervised learning. In Proceedings of the 4th International Symposium on Cyber Security Cryptology and Machine Learning. Springer, Be’er Sheva, Israel, 94–109.
[22]
J. Lorenz, M. Silva, D. Aparício, J. Ascensao, and P. Bizarro. 2020. Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity. In Proceedings of the First ACM International Conference on AI in Finance (ICAIF’20). Association for Computing Machinery, New York, NY.
[23]
S. Nakamoto. 2009. Bitcoin: A Peer-to-Peer Electronic Cash System. Retrieved July 30, 2020 from https://bit.ly/3uLQRNY.
[24]
R. Olson, N. Bartley, R. Urbanowicz, and J. Moore. 2016. Evaluation of a tree-based pipeline optimization tool for automating data science. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, Denver, 485–492.
[25]
M. Ostapowicz and K. Zbikowski. 2019. Detecting fraudulent accounts on blockchain: A supervised approach. In Proceedings of the Web Information Systems Engineering. R. Cheng, N. Mamoulis, Y. Sun, and X. Huang (Eds.), Springer, Hong Kong, 18–31.
[26]
D. Riley. 2020. $25M in cryptocurrency stolen in hack of Lendf.me and Uniswap. Retrieved October 4, 2020 from https://bit.ly/3g8Yrga.
[27]
J. Russell. 2018. The crypto world’s latest hack sees Bancor lose $23.5M. Retrieved October 4, 2020 from https://tcrn.ch/3fKYAaX.
[28]
K. Sedgwick. 2017. One Week On from the Etherdelta Hack, Funds Are Still Being Stolen. Retrieved October 4, 2020 from https://bit.ly/2STgD5u.
[29]
M. Spagnuolo, F. Maggi, and S. Zanero. 2014. BitIodine: Extracting intelligence from the bitcoin network. In Proceedings of the 18th Financial Cryptography and Data Security. N. Christin and R. Safavi-Naini (Eds.), Springer Berlin Heidelberg, Christ Church, Barbados, 457–468.
[30]
J. Valadares, V. Oliveira, J. Sousa, H. Bernardino, A. Vieira, S. Villela, and G. Gonçalves. 2021. Identifying user behavior profiles in ethereum using machine learning techniques. In Proceedings of the IEEE Blockchain Workshop on Blockchain Security, Application, and Performance. IEEE, Melbourne, 327–332.
[31]
J. Wu, Q. Yuan, D. Lin, W. You, W. Chen, C. Chen, and Z. Zheng. 2020. Who are the phishers? Phishing scam detection on ethereum via network embedding. Transactions on Systems, Man, and Cybernetics: Systems 52, 2 (2020), 1–11.
[32]
L. Xu, M. Skoularidou, A. Infante, and K. Veeramachaneni. 2019. Modeling tabular data using conditional GAN. In Proceedings of the 33rd Conference on Neural Information Processing Systems. NIPS proceedings, Vancouver, 1–11.
[33]
M. Zhang, X. Zhang, Y. Zhang, and Z. Lin. 2020. TXSPECTOR: Uncovering attacks in ethereum from transactions. In Proceedings of the 29th USENIX Security Symposium. USENIX Association, Online, 2775–2792.
[34]
R. Zhang, R. Xue, and L. Liu. 2020. Security and privacy on blockchain. ACM Computing Surveys 52, 3 (2020), 1–34. DOI:
[35]
W. Zhao. 2019. Bitpoint Exchange Hacked for $32 Million in Cryptocurrency. Retrieved October 4, 2020 from https://bit.ly/30yY7jP.
[36]
F. Zola, M. Eguimendia, J. Bruse, and R. Urrutia. 2019. Cascading machine learning to attack bitcoin anonymity. In Proceedings of the 2nd International Conference on Blockchain. IEEE, Atlanta, 10–17.

Cited By

View all
  • (2023)Understanding Rug Pulls: An In-depth Behavioral Analysis of Fraudulent NFT CreatorsACM Transactions on the Web10.1145/362337618:1(1-39)Online publication date: 11-Sep-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Distributed Ledger Technologies: Research and Practice
Distributed Ledger Technologies: Research and Practice  Volume 1, Issue 2
December 2022
113 pages
EISSN:2769-6480
DOI:10.1145/3573310
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 December 2022
Online AM: 20 July 2022
Accepted: 29 June 2022
Revised: 07 April 2022
Received: 14 December 2021
Published in DLT Volume 1, Issue 2

Check for updates

Author Tags

  1. Blockchain
  2. ML
  3. suspect identification

Qualifiers

  • Research-article
  • Refereed

Funding Sources

  • National Blockchain Project at IIT Kanpur
  • National Cyber Security Coordinator’s office of the Government of India
  • C3i Center’s funding from the Science and Engineering Research Board of the Government of India

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)186
  • Downloads (Last 6 weeks)17
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Understanding Rug Pulls: An In-depth Behavioral Analysis of Fraudulent NFT CreatorsACM Transactions on the Web10.1145/362337618:1(1-39)Online publication date: 11-Sep-2023

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media