Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach

Authors

  • Shimal Sh. Taher Computer Science Department, University of Duhok, Kurdistan Region, Iraq
  • Siddeeq Y. Ameen Quality Assurance Directorate, Duhok Polytechnic University, Kurdistan Region, Iraq
  • Jihan A. Ahmed Computer Science Department, University of Duhok, Kurdistan Region, Iraq
Volume: 14 | Issue: 1 | Pages: 12822-12830 | February 2024 | https://doi.org/10.48084/etasr.6641

Abstract

In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.

Keywords:

blockchain, Ethereum, fraudulent transactions, machine learning, , Boltzmann factor

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References

F. Allen, X. Gu, and J. Jagtiani, "Fintech, Cryptocurrencies, and CBDC: Financial Structural Transformation in China," Journal of International Money and Finance, vol. 124, Jun. 2022, Art. no. 102625.

A. Raja Santhi and P. Muthuswamy, "Influence of Blockchain Technology in Manufacturing Supply Chain and Logistics," Logistics, vol. 6, no. 1, Mar. 2022, Art. no. 15.

S. Farrugia, J. Ellul, and G. Azzopardi, "Detection of illicit accounts over the Ethereum blockchain," Expert Systems with Applications, vol. 150, Jul. 2020, Art. no. 113318.

P. Bains, Blockchain Consensus Mechanisms: A Primer for Supervisors. Washington, DC, USA: International Monetary Fund, 2022.

M. Ul Hassan, M. H. Rehmani, and J. Chen, "Anomaly Detection in Blockchain Networks: A Comprehensive Survey," IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 289–318, 2023.

Y. Huang and M. Mayer, "Digital currencies, monetary sovereignty, and U.S.–China power competition," Policy & Internet, vol. 14, no. 2, pp. 324–347, 2022.

T. Volety, S. Saini, T. McGhin, C. Z. Liu, and K.-K. R. Choo, "Cracking Bitcoin wallets: I want what you have in the wallets," Future Generation Computer Systems, vol. 91, pp. 136–143, Feb. 2019.

J. Osterrieder, S. Chan, J. Chu, and Y. Zhang, "A Primer on Anomaly and Fraud Detection in Blockchain Networks." SSRN, Rochester, NY, USA, Jan. 04, 2023.

T. Hewa, M. Ylianttila, and M. Liyanage, "Survey on blockchain based smart contracts: Applications, opportunities and challenges," Journal of Network and Computer Applications, vol. 177, Mar. 2021, Art. no. 102857.

P. Sharma, R. Jindal, and M. D. Borah, "Blockchain-based decentralized architecture for cloud storage system," Journal of Information Security and Applications, vol. 62, Nov. 2021, Art. no. 102970.

M. Niranjanamurthy, B. N. Nithya, and S. Jagannatha, "Analysis of Blockchain technology: pros, cons and SWOT," Cluster Computing, vol. 22, no. 6, pp. 14743–14757, Nov. 2019.

G.-T. Nguyen and K. Kim, "A Survey about Consensus Algorithms Used in Blockchain," Journal of Information Processing Systems, vol. 14, no. 1, pp. 101–128, 2018.

J. Bernal Bernabe, J. L. Canovas, J. L. Hernandez-Ramos, R. Torres Moreno, and A. Skarmeta, "Privacy-Preserving Solutions for Blockchain: Review and Challenges," IEEE Access, vol. 7, pp. 164908–164940, 2019.

M. E. Khatib, A. A. Mulla, and W. A. Ketbi, "The Role of Blockchain in E-Governance and Decision-Making in Project and Program Management," Advances in Internet of Things, vol. 12, no. 3, pp. 88–109, Jul. 2022.

A. B. Haque, A. K. M. N. Islam, S. Hyrynsalmi, B. Naqvi, and K. Smolander, "GDPR Compliant Blockchains–A Systematic Literature Review," IEEE Access, vol. 9, pp. 50593–50606, 2021.

N. O. Nawari and S. Ravindran, "Blockchain and the built environment: Potentials and limitations," Journal of Building Engineering, vol. 25, Sep. 2019, Art. no. 100832.

Y. Chen and C. Bellavitis, "Blockchain disruption and decentralized finance: The rise of decentralized business models," Journal of Business Venturing Insights, vol. 13, Jun. 2020, Art. no. e00151 https://doi.org/10.1016/j.jbvi.2019.e00151.

C. Denis Gonzalez, D. Frias Mena, A. Masso Munoz, O. Rojas, and G. Sosa-Gomez, "Electronic Voting System Using an Enterprise Blockchain," Applied Sciences, vol. 12, no. 2, Jan. 2022, Art. no. 531.

K. Toyoda, P. T. Mathiopoulos, I. Sasase, and T. Ohtsuki, "A Novel Blockchain-Based Product Ownership Management System (POMS) for Anti-Counterfeits in the Post Supply Chain," IEEE Access, vol. 5, pp. 17465–17477, 2017.

C. Karapapas, G. Syros, I. Pittaras, and G. C. Polyzos, "Decentralized NFT-based Evolvable Games," in 4th Conference on Blockchain Research & Applications for Innovative Networks and Services, Paris, France, Sep. 2022, pp. 67–74.

N. Sasikala, B. M. Sundaram, S. Biswas, A. Sai Nikhil, and V. S. Rohith, "Survey of latest technologies on Decentralized applications using Blockchain," in Second International Conference on Artificial Intelligence and Smart Energy, Coimbatore, India, Feb. 2022, pp. 1432–1436.

J. P. Cruz, Y. Kaji, and N. Yanai, "RBAC-SC: Role-Based Access Control Using Smart Contract," IEEE Access, vol. 6, pp. 12240–12251, 2018.

F. A. Aponte-Novoa, A. L. S. Orozco, R. Villanueva-Polanco, and P. Wightman, "The 51% Attack on Blockchains: A Mining Behavior Study," IEEE Access, vol. 9, pp. 140549–140564, 2021.

G. Alicioglu and B. Sun, "A survey of visual analytics for Explainable Artificial Intelligence methods," Computers & Graphics, vol. 102, pp. 502–520, Feb. 2022.

A. Barredo Arrieta et al., "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 58, pp. 82–115, Jun. 2020.

B. Mahbooba, M. Timilsina, R. Sahal, and M. Serrano, "Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model," Complexity, vol. 2021, Jan. 2021, Art. no. e6634811.

T. Zahavy, N. Ben-Zrihem, and S. Mannor, "Graying the black box: Understanding DQNs," in 33 rd International Conference on Machine Learning, New York, NY, USA, Jun. 2016, pp. 1899–1908.

M. T. Ribeiro, S. Singh, and C. Guestrin, "‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier," in 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, Aug. 2016, pp. 1135–1144.

O. Sagi and L. Rokach, "Ensemble learning: A survey," WIREs Data Mining and Knowledge Discovery, vol. 8, no. 4, 2018, Art. no. e1249.

A. Khatri, S. Agrawal, and J. M. Chatterjee, "Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach," Scientific Programming, vol. 2022, Feb. 2022, Art. no. e2626868.

M. U. Salur and I. Aydın, "A soft voting ensemble learning-based approach for multimodal sentiment analysis," Neural Computing and Applications, vol. 34, no. 21, pp. 18391–18406, Nov. 2022.

G. P. de Oliveira, A. Fonseca, and P. C. Rodrigues, "Diabetes diagnosis based on hard and soft voting classifiers combining statistical learning models," Brazilian Journal of Biometrics, vol. 40, no. 4, pp. 415–427, Dec. 2022.

A. Khanna et al., "Blockchain: Future of e-Governance in Smart Cities," Sustainability, vol. 13, no. 21, Jan. 2021, Art. no. 11840.

M. Ostapowicz and K. Zbikowski, "Detecting Fraudulent Accounts on Blockchain: A Supervised Approach," in International Conference on Web Information Systems Engineering, Hong Kong, China, Jan. 2020, pp. 18–31.

P. N. Sureshbhai, P. Bhattacharya, and S. Tanwar, "KaRuNa: A Blockchain-Based Sentiment Analysis Framework for Fraud Cryptocurrency Schemes," in IEEE International Conference on Communications Workshops, Dublin, Ireland, Jun. 2020, pp. 1–6.

M. Bhowmik, T. Sai Siri Chandana, and B. Rudra, "Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain," in 5th International Conference on Computing Methodologies and Communication, Erode, India, Apr. 2021, pp. 539–541.

B. Chen, F. Wei, and C. Gu, "Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms," Security and Communication Networks, vol. 2021, Feb. 2021, Art. no. e6643763.

A. H. H. Kabla, M. Anbar, S. Manickam, and S. Karupayah, "Eth-PSD: A Machine Learning-Based Phishing Scam Detection Approach in Ethereum," IEEE Access, vol. 10, pp. 118043–118057, 2022.

A. A. Amponsah, A. F. Adekoya, and B. A. Weyori, "A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology," Decision Analytics Journal, vol. 4, Sep. 2022, Art. no. 100122.

W. Wang, J. Song, G. Xu, Y. Li, H. Wang, and C. Su, "ContractWard: Automated Vulnerability Detection Models for Ethereum Smart Contracts," IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1133–1144, Apr. 2021.

V. Aliyev, "Ethereum Fraud Detection Dataset." kaggle, 2020, [Online]. Available: https://www.kaggle.com/datasets/vagifa/ethereum-frauddetection-dataset.

Y. Elmougy and O. Manzi, "Anomaly Detection on Bitcoin, Ethereum Networks Using GPU-accelerated Machine Learning Methods," in 31st International Conference on Computer Theory and Applications, Alexandria, Egypt, Dec. 2021, pp. 166–171.

O. M. Ahmed, L. M. Haji, A. M. Ahmed, and N. M. Salih, "Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11735–11738, Oct. 2023.

N. K. Al-Shammari, T. H. Syed, and M. B. Syed, "An Edge – IoT Framework and Prototype based on Blockchain for Smart Healthcare Applications," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7326–7331, Aug. 2021.

K. Rajeshkumar, C. Ananth, and N. Mohananthini, "Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10978–10983, Jun. 2023.

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How to Cite

[1]
S. S. Taher, S. Y. Ameen, and J. A. Ahmed, “Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12822–12830, Feb. 2024.

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