Conserving Certainty of Crypto Transactions with Machine Learning Methodologies

S. Haripriya; Dr. D. Banumathy; Dr. A. Jeyamurugan; Dr. G. Madasamyraja

doi:10.32628/IJSRSET24113131

Authors

S. Haripriya Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, India Author
Dr. D. Banumathy Professor, Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, India Author
Dr. A. Jeyamurugan Professor, Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, India Author
Dr. G. Madasamyraja Professor, Department of Information Technology, Paavai Engineering College, Namakkal, Tamilnadu, India Author

DOI:

https://doi.org/10.32628/IJSRSET24113131

Keywords:

Cryptocurrencies Transactions, Preventing Fradulent Activities, Future Prediction

Abstract

Nowadays there are increase in fraudulent activities within cryptocurrencies transactions. To combat this, we propose a novel framework that integrates machine learning methodologies with the SHA-256 algorithm to enhance security and predict price fluctuations. This framework aims to provide a comprehensive solution for preventing fraudulent activities in cryptocurrencies transactions contributing to a more secure.

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References

M. Ul Hassan, M. H. Rehmani, and J. Chen, ‘‘Anomaly detection in blockchain networks: A comprehensive survey,’’ IEEE Commun. Surveys Tuts., vol. 25, no. 1, pp. 289–318, 1st Quart., 2023, doi: 10.1109/COMST.2022.3205643. DOI: https://doi.org/10.1109/COMST.2022.3205643

P. Raghavan and N. E. Gayar, "Fraud Detection using Machine Learning and Deep Learning", 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 334-339, 2020, [online] Available: https://doi.org/10.1109/ICCIKE47802.2019.9004231. DOI: https://doi.org/10.1109/ICCIKE47802.2019.9004231

Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M. and Anderla, A., 2019, March. Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/INFOTEH.2019.8717766

B. Kiliç, A. Sen, and C. Özturan, ‘‘Fraud detection in blockchains using machine learning,’’ in Proc. 4th Int. Conf. Blockchain Comput. Appl. (BCCA), Sep. 2022, pp. 214–218, doi: 10.1109/BCCA55292. 2022.9922045. DOI: https://doi.org/10.1109/BCCA55292.2022.9922045

S. A. Alsaif, ‘‘Machine learning-based ransomware classification of Bitcoin transactions,’’ Appl. Comput. Intell. Soft Comput., vol. 2023, Jan. 2023, Art. no. 6274260, doi: 10.1155/2023/6274260. DOI: https://doi.org/10.1155/2023/6274260

C. Ozturan, A. Sen and B. Kilic, "Transaction Graph Dataset for the Ethereum Blockchain", Zenodo, pp. 4718440, Apr. 2021.

Time-series Bitcoin price predictions with high-dimensional features using machine learningMohammedMudassir1 Devrim Unal1 Shada Bennbaia 20 June Mohammad Hammoudeha division of Springer Nature 2020 is Springer-Verlag London Ltd.

H. Sebastião and P. Godinho, “Forecasting and trading cryptocurrencies with machine learning under changing market conditions,” Financ. Innov., vol. 7, no. 1, pp. 1–30, 2021. DOI: https://doi.org/10.1186/s40854-020-00217-x

Elbaghdadi A, Mezroui S, El Oualkadi A (2021) K-Nearest Neighbors Algorithm (KNN): An Approach to Detect Illicit Transaction in the Bitcoin Network. In: Integration Challenges for Analytics, Business Intelligence, and Data Mining: IGI Global, 2021, pp 161–178. DOI: https://doi.org/10.4018/978-1-7998-5781-5.ch008

Chen B, Fushan W, Chunxiang G (2021) Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms. Security and Communication Networks 2021. DOI: https://doi.org/10.1155/2021/6643763