Fortifying the Blockchain Fortress: A Machine Learning Paradigm for Enhanced Security

Year : 2024 | Volume : 11 | Issue : 02 | Page : 33 40
    By

    Ramya B.N.,

  • Vani,

  • Ushashri Gunti,

  • Tara V.K.,

  • Hina Nazneen,

  1. Assistant Professor, Department of Artificial Intelligence and Machine Learning, Jyothy Institute of Technology, Bengaluru, Karnataka, India
  2. Student, Department of Information Science and Engineering, City Engineering College, Bengaluru, Karnataka, India
  3. Assistant Professor, Department of Artificial Intelligence and Machine Learning, Jyothy Institute of Technology, Bengaluru, Karnataka, India
  4. Student, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India
  5. Student, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India

Abstract

Blockchain technology has emerged as a revolutionary tool in the digital landscape, enabling secure and transparent transactions across a decentralized network. Despite its robust security features, blockchain systems remain vulnerable to anomalies and malicious activities. The detection of these anomalies using machine learning has become essential for protecting blockchain networks and ensuring their integrity. This project delves into the application of machine learning techniques to detect abnormal patterns within blockchain data, with a specific focus on utilizing the XGBoost classifier. By analyzing large volumes of blockchain transactions, the XGBoost classifier is employed to identify irregularities that could indicate potential security threats. Our approach achieved an accuracy of 84%, demonstrating the efficacy of machine learning in detecting and mitigating these threats. Furthermore, this research underscores the pivotal role of machine learning in enhancing the resilience and reliability of blockchain networks. By proactively identifying anomalies, machine learning helps to maintain the trustworthiness and stability of blockchain systems, contributing to a safer digital ecosystem. The findings of this project not only highlight the importance of advanced analytics in cybersecurity but also pave the way for future innovations in safeguarding decentralized technologies.

Keywords: Blockchain networks, machine learning, XGBoost classifier, anomaly detection, blockchain transactions, artificial intelligence

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Ramya B.N., Vani, Ushashri Gunti, Tara V.K., Hina Nazneen. Fortifying the Blockchain Fortress: A Machine Learning Paradigm for Enhanced Security. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):33-40.
How to cite this URL:
Ramya B.N., Vani, Ushashri Gunti, Tara V.K., Hina Nazneen. Fortifying the Blockchain Fortress: A Machine Learning Paradigm for Enhanced Security. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):33-40. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155824


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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 01/05/2024
Accepted 14/05/2024
Published 10/07/2024
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