Machine Learning Applications in Cryptocurrency: Detection, Prediction, and Behavioral Analysis of Bitcoin Market and Scam Activities in the USA
DOI:
https://doi.org/10.22399/ijsusat.8Keywords:
Cryptocurrency, Bitcoin, Suspicious Activities, Money Laundering, Fraud Detection, Machine LearningAbstract
The rapid evolution of cryptocurrency markets, coupled with the escalating sophistication of fraudulent activities, has amplified the necessity for advanced machine learning (ML) methodologies to augment the detection, prediction, and behavioral analysis of Bitcoin transactions. Conventional approaches to fraud detection and market analysis frequently falter in capturing cryptocurrency ecosystems' intricate, dynamic, and exceedingly volatile essence. This research elucidates a data-driven framework that employs machine learning to identify scams, forecast Bitcoin market fluctuations, and scrutinize user behavior patterns within the U.S. cryptocurrency domain. By leveraging extensive Bitcoin transaction datasets enriched with features such as transaction volumes, timestamps, wallet activities, and anomaly indicators, the study deploys a diverse array of models: Random Forest, XGBoost, Logistic Regression, Support Vector Machines (SVMs), Graph Neural Networks (GNNs), Isolation Forest, and Autoencoders for fraud detection; Long Short-Term Memory (LSTM) networks and Deep Q-Learning for price prediction and trend forecasting; and K-Means clustering for the behavioral analysis of user activities. The study integrates time-series analysis, anomaly detection pipelines, and dimensionality reduction techniques to enhance predictive robustness and address challenges such as pronounced volatility, concept drift, and data sparsity. Moreover, the data imbalance issues intrinsic to fraud detection are confronted through strategic resampling methodologies. Model performance is meticulously assessed utilizing metrics such as Accuracy, Precision, Recall, F1-Score, ROC-AUC, and RMSE for forecasting endeavors.
References
[1] Alarifi, A., et al. (2024). Machine Learning Approaches for Early Scam Detection in Cryptocurrency Trading. Computers & Security, 125, 102973.
[2] Bhowmik, P. K., et al. (2025). AI-Driven Sentiment Analysis for Bitcoin Market Trends: A Predictive Approach to Crypto Volatility. Journal of Ecohumanism, 4(4), 266-288.
[3] Chen, Y., Zhao, X., & Zhang, W. (2024). Anomaly Detection in DeFi Transactions Using Autoencoder-Based Machine Learning Models. IEEE Access, 12, 108772-108783.
[4] Chen, Y., Zhao, X., & Zhang, W. (2024). Detecting Financial Fraud in Blockchain Networks Using Deep Anomaly Detection Techniques. IEEE Transactions on Emerging Topics in Computing, 12(2), 345-358.
[5] Das, B. C., et al. (2025). Detecting Cryptocurrency Scams in the USA: A Machine Learning-Based Analysis of Scam Patterns and Behaviors. Journal of Ecohumanism, 4(2), 2091-2111.
[6] Islam, M. S., et al. (2025). Machine Learning-Based Cryptocurrency Prediction: Enhancing Market Forecasting with Advanced Predictive Models. Journal of Ecohumanism, 4(2), 2498-2519.
[7] Islam, M. Z., et al. (2025). Machine Learning-Based Detection and Analysis of Suspicious Activities in Bitcoin Wallet Transactions in the USA. Journal of Ecohumanism, 4(1), 3714-3734.
[8] Lee, D., & Moon, S. (2025). Graph Neural Networks for Fraudulent Transaction Detection in Bitcoin Networks. Applied Intelligence, 65(2), 345-359.
[9] Lee, S., & Kwon, Y. (2024). Regulatory Implications of AI-Driven Fraud Detection in Cryptocurrency Markets. Journal of Financial Regulation and Compliance, 32(1), 45-62.
[10] Lin, J., & Wang, L. (2025). Behavioral Clustering of Bitcoin Users Using Unsupervised Learning Techniques. Computers & Security, 137, 103175.
[11] Park, S., & Kim, J. (2024). Applying Deep Q-Learning to Cryptocurrency Trading Strategies. Expert Systems with Applications, 235, 120314.
[12] Zhang, H., & Liu, Z. (2024). Behavioral Clustering and Fraudulent Node Detection in Bitcoin Transaction Networks. Expert Systems with Applications, 215, 119273.
[13] LAVUDIYA, N. S., & C.V.P.R Prasad. (2024). Enhancing Ophthalmological Diagnoses: An Adaptive Ensemble Learning Approach Using Fundus and OCT Imaging. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.678
[14] Pata, U. K. (2025). Machine Learning in Energy Technology and Infrastructure: Predictive Insights for Renewable Generation and Low-Carbon Trade in the USA. International Journal of Sustainable Science and Technology, 1(1). https://doi.org/10.22399/ijsusat.10
[15] N.B. Mahesh Kumar, T. Chithrakumar, T. Thangarasan, J. Dhanasekar, & P. Logamurthy. (2025). AI-Powered Early Detection and Prevention System for Student Dropout Risk. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.839
[16] Chui, K. T. (2025). Artificial Intelligence in Energy Sustainability: Predicting, Analyzing, and Optimizing Consumption Trends. International Journal of Sustainable Science and Technology, 1(1). https://doi.org/10.22399/ijsusat.1
[17] E.V.N. Jyothi, Jaibir Singh, Suman Rani, A. Malla Reddy, V. Thirupathi, Janardhan Reddy D, & M. Bhavsingh. (2025). Machine Learning-Based Optimization for 5G Resource Allocation Using Classification and Regression Techniques. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1657
[18] Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.18
[19] P. Rathika, S. Yamunadevi, P. Ponni, V. Parthipan, & P. Anju. (2024). Developing an AI-Powered Interactive Virtual Tutor for Enhanced Learning Experiences. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.782
[20] Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.19
[21]K. Tamilselvan, , M. N. S., A. Saranya, D. Abdul Jaleel, Er. Tatiraju V. Rajani Kanth, & S.D. Govardhan. (2025). Optimizing data processing in big data systems using hybrid machine learning techniques. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.936
[22]Fowowe, O. O., & Agboluaje, R. (2025). Leveraging Predictive Analytics for Customer Churn: A Cross-Industry Approach in the US Market. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.20
[23]Poondy Rajan Y, Kishore Kunal, Anitha Palanisamy, Senthil Kumar Rajendran, Rupesh Gupta, & Madeshwaren, V. (2025). Machine Learning Framework for Detecting Fake News and Combating Misinformation Spread on Facebook Platforms. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1492
[24]Hafez, I. Y., & El-Mageed, A. A. A. (2025). Enhancing Digital Finance Security: AI-Based Approaches for Credit Card and Cryptocurrency Fraud Detection. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.21
[25]Wang, S., & Koning, S. bin I. (2025). Social and Cognitive Predictors of Collaborative Learning in Music Ensembles . International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.806
[26]García, R., Carlos Garzon, & Juan Estrella. (2025). Generative Artificial Intelligence to Optimize Lifting Lugs: Weight Reduction and Sustainability in AISI 304 Steel. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.22
[27]Anakal, S., K. Krishna Prasad, Chandrashekhar Uppin, & M. Dileep Kumar. (2025). Diagnosis, visualisation and analysis of COVID-19 using Machine learning . International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.826
[28]Fabiano de Abreu Agrela Rodrigues. (2025). DWRI as a New Neurobiological Perspective of Global Intelligence: From Synaptic Connectivity to Subjective Creativity. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.24
[29]S. Esakkiammal, & K. Kasturi. (2024). Advancing Educational Outcomes with Artificial Intelligence: Challenges, Opportunities, And Future Directions. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.799
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Saru Kumari

This work is licensed under a Creative Commons Attribution 4.0 International License.