Interpretable Data Analytics in Blockchain Networks Using Variational Autoencoders and Model-Agnostic Explanation Techniques for Enhanced Anomaly Detection
DOI:
https://doi.org/10.32628/IJSRST24116170Keywords:
Blockchain, Anomaly Detection, Variational Autoencoders, Local Interpretable Model-agnostic Explanations, Network Attacks, Detection Techniques Analysis, EDSAbstract
The rapid growth of blockchain technology has brought about increased transaction volumes and complexity, leading to challenges in detecting fraudulent activities and understanding data patterns. Traditional data analytics approaches often fall short in providing both accurate anomaly detection and interpretability, especially in decentralized environments. This paper explores the integration of Variational Autoencoders (VAEs), a deep learning-based anomaly detection technique, with model-agnostic explanation methods such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to enhance the interpretability of blockchain data analytics. Variational Autoencoders are leveraged to capture the underlying distribution of blockchain transactions, identifying anomalies by modeling deviations from learned patterns. To address the often-opaque nature of deep learning models, SHAP and LIME are employed to provide post-hoc explanations, offering insights into the key factors influencing the model’s predictions. This hybrid approach aims to not only detect irregularities in blockchain networks effectively but also to make the decision-making process transparent and understandable for stakeholders. By combining advanced anomaly detection with interpretable machine learning, this study presents a robust framework for improving the security and reliability of blockchain-based systems, providing a valuable tool for both developers and analysts in mitigating risks and enhancing trust in decentralized applications.
Downloads
References
Ajayi, A. A., Igba, E., Soyele, A. D., & Enyejo, J. O. (2024). Enhancing Digital Identity and Financial Security in Decentralized Finance (Defi) through Zero-Knowledge Proofs (ZKPs) and Blockchain Solutions for Regulatory Compliance and Privacy. OCT 2024 |IRE Journals | Volume 8 Issue 4 | ISSN: 2456-8880
Ajayi, A. A., Igba, E., Soyele, A. D., & Enyejo, J. O. (2024). Quantum Cryptography and Blockchain-Based Social Media Platforms as a Dual Approach to Securing Financial Transactions in CBDCs and Combating Misinformation in U.S. Elections. International Journal of Innovative Science and Research Technology. Volume 9, Issue 10, Oct.– 2024 ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24OCT1697. DOI: https://doi.org/10.38124/ijisrt/IJISRT24OCT1697
Akindote, O., Enyejo, J. O., Awotiwon, B. O. & Ajayi, A. A. (2024). Integrating Blockchain and Homomorphic Encryption to Enhance Security and Privacy in Project Management and Combat Counterfeit Goods in Global Supply Chain Operations. International Journal of Innovative Science and Research Technology Volume 9, Issue 11, NOV. 2024, ISSN No:-2456-2165. https://doi.org/10.38124/ijisrt/IJISRT24NOV149. DOI: https://doi.org/10.38124/ijisrt/IJISRT24NOV149
Akindotei, O., Igba E., Awotiwon, B. O., & Otakwu, A (2024). Blockchain Integration in Critical Systems Enhancing Transparency, Efficiency, and Real-Time Data Security in Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management. International Journal of Scientific Research and Modern Technology (IJSRMT) Volume 3, Issue 11, 2024. DOI: 10.38124/ijsrmt.v3i11.107. DOI: https://doi.org/10.38124/ijsrmt.v3i11.107
Amann, J., Gudehus, J., & Franke, D. (2023). Advances in blockchain data analytics: Interpretability using SHAP and LIME. Journal of Machine Learning and Blockchain, 8(2), 145-158. https://doi.org/10.1109/MLB.2023.0001
Awotiwon, B. O., Enyejo, J. O., Owolabi, F. R. A., Babalola, I. N. O., & Olola, T. M. (2024). Addressing Supply Chain Inefficiencies to Enhance Competitive Advantage in Low-Cost Carriers (LCCs) through Risk Identification and Benchmarking Applied to Air Australasia’s Operational Model. World Journal of Advanced Research and Reviews, 2024, 23(03), 355–370. https://wjarr.com/content/addressing-supply-chain-inefficiencies-enhance-competitive-advantage-low-cost-carriers-lccs DOI: https://doi.org/10.30574/wjarr.2024.23.3.2684
Ayoola, V. B., Idoko, P. I., Danquah, E. O., Ukpoju, E. A., Obasa, J., Otakwu, A. & Enyejo, J. O. (2024). Optimizing Construction Management and Workflow Integration through Autonomous Robotics for Enhanced Productivity Safety and Precision on Modern Construction Sites. International Journal of Scientific Research and Modern Technology (IJSRMT). Vol 3, Issue 10, 2024. https://www.ijsrmt.com/index.php/ijsrmt/article/view/56 DOI: https://doi.org/10.38124/ijsrmt.v3i10.56
Balogun, T. K., Enyejo, J. O., Ahmadu, E. O., Akpovino, C. U., Olola, T. M., & Oloba, B. L. (2024). The Psychological Toll of Nuclear Proliferation and Mass Shootings in the U.S. and How Mental Health Advocacy Can Balance National Security with Civil Liberties. IRE Journals, Volume 8 Issue 4, ISSN: 2456-8880.
Bhattacharya, A. (2022). Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more. Packt Publishing Ltd.
Carmichael, Z. (2024). Explainable AI for High-stakes Decision-making (Doctoral dissertation, University of Notre Dame).
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832. https://doi.org/10.3390/electronics8080832 DOI: https://doi.org/10.3390/electronics8080832
Carvalho, D. V., Pereira, E. P., Cardoso, J. S., & Silva, D. F. (2019). Machine learning interpretability: A survey on methods and metrics. ACM Computing Surveys, 52(5), 1–43. https://doi.org/10.1145/3236009 DOI: https://doi.org/10.1145/3236009
Cholevas, C., Angeli, E., Sereti, Z., Mavrikos, E., & Tsekouras, G. E. (2024). Anomaly detection in blockchain networks using unsupervised learning: A survey. Algorithms, 17(5), 201. https://doi.org/10.3390/a17050201 DOI: https://doi.org/10.3390/a17050201
Demertzis, K., Iliadis, L., Tziritas, N., & Kikiras, P. (2020). Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Computing and Applications, 32(23), 17361-17378. DOI: https://doi.org/10.1007/s00521-020-05189-8
Ebenibo, L., Enyejo, J. O., Addo, G., & Olola, T. M. (2024). Evaluating the Sufficiency of the data protection act 2023 in the age of Artificial Intelligence (AI): A comparative case study of Nigeria and the USA. International Journal of Scholarly Research and Reviews, 2024, 05(01), 088–107. https://srrjournals.com/ijsrr/content/evaluating-sufficiency-data-protection-act-2023-age-artificial-intelligence-ai-comparative DOI: https://doi.org/10.56781/ijsrr.2024.5.1.0044
Enyejo, J. O., Adeyemi, A. F., Olola, T. M., Igba, E & Obani, O. Q. (2024). Resilience in supply chains: How technology is helping USA companies navigate disruptions. Magna Scientia Advanced Research and Reviews, 2024, 11(02), 261–277. https://doi.org/10.30574/msarr.2024.11.2.0129 DOI: https://doi.org/10.30574/msarr.2024.11.2.0129
Enyejo, J. O., Babalola, I. N. O., Owolabi, F. R. A. Adeyemi, A. F., Osam-Nunoo, G., & Ogwuche, A. O. (2024). Data-driven digital marketing and battery supply chain optimization in the battery powered aircraft industry through case studies of Rolls-Royce’s ACCEL and Airbus's E-Fan X Projects. International Journal of Scholarly Research and Reviews, 2024, 05(02), 001–020. https://doi.org/10.56781/ijsrr.2024.5.2.0045 DOI: https://doi.org/10.56781/ijsrr.2024.5.2.0045
Enyejo, J. O., Balogun, T. K., Klu, E. Ahmadu, E. O., & Olola, T. M. (2024). The Intersection of Traumatic Brain Injury, Substance Abuse, and Mental Health Disorders in Incarcerated Women Addressing Intergenerational Trauma through Neuropsychological Rehabilitation. American Journal of Human Psychology (AJHP). Volume 2 Issue 1, Year 2024 ISSN: 2994-8878 (Online). https://journals.e-palli.com/home/index.php/ajhp/article/view/383 DOI: https://doi.org/10.54536/ajhp.v2i1.3830
Enyejo, L. A., Adewoye, M. B. & Ugochukwu, U. N. (2024). Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. Vol. 10 No. 6 (2024): November-December doi : https://doi.org/10.32628/CSEIT24106185
Enyejo, J. O., Obani, O. Q, Afolabi, O. Igba, E. & Ibokette, A. I., (2024). Effect of Augmented Reality (AR) and Virtual Reality (VR) experiences on customer engagement and purchase behavior in retail stores. Magna Scientia Advanced Research and Reviews, 2024, 11(02), 132–150. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0116.pdf DOI: https://doi.org/10.30574/msarr.2024.11.2.0116
Hasan, M., Rahman, M. S., Janicke, H., & Sarker, I. H. (2024). Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis. Blockchain: Research and Applications, 100207. DOI: https://doi.org/10.1016/j.bcra.2024.100207
Hassan, M. U., Rehmani, M. H., & Chen, J. (2022). Anomaly detection in blockchain networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 25(1), 289-318. DOI: https://doi.org/10.1109/COMST.2022.3205643
Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Akoh, O., & Ileanaju, S. (2024). Harmonizing the voices of AI: Exploring generative music models, voice cloning, and voice transfer for creative expression.
Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Akoh, O., & Isenyo, G. (2024). Integrating superhumans and synthetic humans into the Internet of Things (IoT) and ubiquitous computing: Emerging AI applications and their relevance in the US context. *Global Journal of Engineering and Technology Advances*, 19(01), 006-036. DOI: https://doi.org/10.30574/gjeta.2024.19.1.0055
Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Ugbane, S. I., Akoh, O., & Odeyemi, M. O. (2024). Exploring the potential of Elon Musk's proposed quantum AI: A comprehensive analysis and implications. *Global Journal of Engineering and Technology Advances*, 18(3), 048-065. DOI: https://doi.org/10.30574/gjeta.2024.18.3.0037
Igba, E., Adeyemi, A. F., Enyejo, J. O., Ijiga, A. C., Amidu, G., & Addo, G. (2024). Optimizing Business loan and Credit Experiences through AI powered ChatBot Integration in financial services. Finance & Accounting Research Journal, P-ISSN: 2708-633X, E-ISSN: 2708, Volume 6, Issue 8, P.No. 1436-1458, August 2024. DOI:10.51594/farj.v6i8.1406 DOI: https://doi.org/10.51594/farj.v6i8.1406
Igba, E., Danquah, E. O., Ukpoju, E. A., Obasa, J., Olola, T. M., & Enyejo, J. O. (2024). Use of Building Information Modeling (BIM) to Improve Construction Management in the USA. World Journal of Advanced Research and Reviews, 2024, 23(03), 1799–1813. https://wjarr.com/content/use-building-information-modeling-bim-improve-construction-management-usa DOI: https://doi.org/10.30574/wjarr.2024.23.3.2794
Ijiga, A. C., Aboi, E. J., Idoko, P. I., Enyejo, L. A., & Odeyemi, M. O. (2024). Collaborative innovations in Artificial Intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking. Global Journal of Engineering and Technology Advances, 2024,18(03), 106-123. https://gjeta.com/sites/default/files/GJETA-2024-0046.pdf DOI: https://doi.org/10.30574/gjeta.2024.18.3.0046
Ijiga, A. C., Abutu E. P., Idoko, P. I., Ezebuka, C. I., Harry, K. D., Ukatu, I. E., & Agbo, D. O. (2024). Technological innovations in mitigating winter health challenges in New York City, USA. International Journal of Science and Research Archive, 2024, 11(01), 535–551.· https://ijsra.net/sites/default/files/IJSRA-2024-0078.pdf DOI: https://doi.org/10.30574/ijsra.2024.11.1.0078
Ijiga, A. C., Abutu, E. P., Idoko, P. I., Agbo, D. O., Harry, K. D., Ezebuka, C. I., & Umama, E. E. (2024). Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America. International Journal of Biological and Pharmaceutical Sciences Archive, 2024, 07(01), 048–063. https://ijbpsa.com/sites/default/files/IJBPSA-2024-0015.pdf DOI: https://doi.org/10.53771/ijbpsa.2024.7.1.0015
Ijiga, A. C., Enyejo, L. A., Odeyemi, M. O., Olatunde, T. I., Olajide, F. I & Daniel, D. O. (2024). Integrating community-based partnerships for enhanced health outcomes: A collaborative model with healthcare providers, clinics, and pharmacies across the USA. Open Access Research Journal of Biology and Pharmacy, 2024, 10(02), 081–104. https://oarjbp.com/content/integrating-community-based-partnerships-enhanced-health-outcomes-collaborative-model DOI: https://doi.org/10.53022/oarjbp.2024.10.2.0015
Ijiga, A. C., Olola, T. M., Enyejo, L. A., Akpa, F. A., Olatunde, T. I., & Olajide, F. I. (2024). Advanced surveillance and detection systems using deep learning to combat human trafficking. Magna Scientia Advanced Research and Reviews, 2024, 11(01), 267–286. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0091.pdf.
Ijiga, A. C., Olola, T. M., Enyejo, L. A., Akpa, F. A., Olatunde, T. I., & Olajide, F. I. (2024). Advanced surveillance and detection systems using deep learning to combat human trafficking. Magna Scientia Advanced Research and Reviews, 2024, 11(01), 267–286. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0091.pdf DOI: https://doi.org/10.30574/msarr.2024.11.1.0091
Kell, D. et al., (2023). Deep learning and generative methods in cheminformatics and chemical biology: Navigating small molecule space intelligently. https://www.researchgate.net/figure/ariational-autoencoder-networks-and-their-uses-A-Basic-VAE-architecture-showing-the_fig4_347531998
Khan, S., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411. https://doi.org/10.1016/j.future.2017.11.022 DOI: https://doi.org/10.1016/j.future.2017.11.022
Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. Proceedings of the International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=ZbWvA7A3V
Kingma, D. P., & Welling, M. (2013). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=Z5xJw9Sc57A
Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. Proceedings of the International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.1312.6114
Laridi, S., Palmer, G., & Tam, K. M. M. (2024). Enhanced federated anomaly detection through autoencoders using summary statistics-based thresholding. arXiv:2410.09284. https://doi.org/10.48550/arxiv.2410.09284 DOI: https://doi.org/10.1038/s41598-024-76961-2
Li, Y., Jiang, X., Chen, Y., & Luo, X. (2019). A survey on the security of blockchain systems. Future Generation Computer Systems, 107, 841–853. https://doi.org/10.1016/j.future.2017.08.020 DOI: https://doi.org/10.1016/j.future.2017.08.020
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1705.07874
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, 4768–4777. https://doi.org/10.1145/3422622.3422637
Mavink, (2022). The Impact of Blockchain Technology on Supply Chain Management | Bruce. https://mavink.com/post/A439DDE104E143DD2C730F24323A41CAB0AMD2D19F/blockchain-use-case-diagrams
Meenasian, S. (2021). Digital Transformation. https://www.mitel.com/blog/is-a-business-continuity-plan-really-worth-it
Nguyen, G., & Kim, K. (2018). A survey about consensus algorithms used in blockchain. Journal of Information Processing Systems, 14(1), 101–128. https://doi.org/10.3745/JIPS.03.0093
Okeke, R. O., Ibokette, A. I., Ijiga, O. M., Enyejo, L. A., Ebiega, G. I., & Olumubo, O. M. (2024). The reliability assessment of power transformers. *Engineering Science & Technology Journal*, 5(4), 1149-1172. DOI: https://doi.org/10.51594/estj.v5i4.981
Owolabi, F. R. A., Enyejo, J. O., Babalola, I. N. O., & Olola, T. M. (2024). Overcoming engagement shortfalls and financial constraints in Small and Medium Enterprises (SMES) social media advertising through cost-effective Instagram strategies in Lagos and New York City. International Journal of Management & Entrepreneurship Research P-ISSN: 2664-3588, E-ISSN: 2664-3596. DOI: 10.51594/ijmer.v6i8.1462 DOI: https://doi.org/10.51594/ijmer.v6i8.1462
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778 DOI: https://doi.org/10.1145/2939672.2939778
Sharma, S., & Jain, P. (2023). Explaining blockchain anomaly detection through SHAP and LIME: Enhancing trust in AI-driven decisions. AI and Ethics Journal, 15(4), 127-140. https://doi.org/10.1007/s43694-023-00064-x
Siddamsetti, S., Tejaswi, C., & Maddula, P. (2024). Anomaly detection in blockchain using machine learning. Journal of Electrical Systems, 20(3), 619-634. DOI: https://doi.org/10.52783/jes.2988
Tam, K. M. M., & others. (2024). Federated learning with anomaly detection via gradient and reconstruction analysis. arXiv:2403.10000. https://doi.org/10.48550/arxiv.2403.10000
Ugbane, S. I., Umeaku, C., Idoko, I. P., Enyejo, L. A., Michael, C. I. & Efe, F. (2024). Optimization of Quadcopter Propeller Aerodynamics Using Blade Element and Vortex Theory. International Journal of Innovative Science and Research Technology.Volume 9, Issue 10, October– 2024 ISSN No:-2456-2165. https://doi.org/10.38124/ijisrt/IJISRT24OCT1820 DOI: https://doi.org/10.38124/ijisrt/IJISRT24OCT1820
Xie, H., Liu, Y., & Li, X. (2018). Blockchain data analysis and visualization: A survey. Future Generation Computer Systems, 89, 73–84. https://doi.org/10.1016/j.future.2018.07.013 DOI: https://doi.org/10.1016/j.future.2018.07.013
Xu, L., Li, Y., & Ma, X. (2020). Blockchain anomaly detection using variational autoencoders. Journal of Computational Science, 42, 101090. https://doi.org/10.1016/j.jocs.2020.101090 DOI: https://doi.org/10.1016/j.jocs.2020.101090
Yin, H., Zhang, Z., He, J., Ma, L., Zhu, L., Li, M., & Khoussainov, B. (2021). Proof of continuous work for reliable data storage over permissionless blockchain. IEEE Internet of Things Journal, 9(10), 7866-7875. DOI: https://doi.org/10.1109/JIOT.2021.3115568
Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology?—A systematic review. PLoS ONE, 11(10), e0163477. https://doi.org/10.1371/journal.pone.0163477 DOI: https://doi.org/10.1371/journal.pone.0163477
Yue, Y., Zhang, J., Zhang, M., & Yang, J. (2024). An abnormal account identification method by topology feature analysis for blockchain-based transaction network. Electronics, 13(8), 1416. https://doi.org/10.3390/electronics13081416 DOI: https://doi.org/10.3390/electronics13081416
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352–375. https://doi.org/10.1504/IJWGS.2018.095647 DOI: https://doi.org/10.1504/IJWGS.2018.095647
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.