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DOI: ENHANCING BLOCKCHAIN SECURITY WITH MACHINE LEARNING: A COMPREHENSIVE STUDY OF ALGORITHMS AND APPLICATIONS
Ashim Chandra Das , Master of Science in Information Technology, Washington University of Science and Technology, USA S M Shadul Islam Rishad , Master Of Science in Information Technology, Westcliff University, USA Pinky Akter , Master of Science in Information Technology, Washington University of Science and Technology, USA Sanjida Akter Tisha , Master of Science in Information Technology, Washington University of Science and Technology, USA Sadia Afrin , Department of Computer & Information Science, Gannon University, USA Farhan Shakil , Master’s in Cybersecurity Operations, Webster University, Saint Louis, MO, USA Pritom Das , College of Computer Science, Pacific States University, Los Angeles, CA, USA Mashaeikh Zaman Md. Eftakhar Choudhury , Master of Social Science in Security Studies, Bangladesh University of Professional (BUP), Dhaka Md Mohibur Rahman , Fred DeMatteis School of Engineering and Applied Science, Hofstra University, USAAbstract
Blockchain technology offers secure, decentralized systems but faces increasing threats like double-spending and Sybil attacks. This study evaluates machine learning algorithms, including Random Forest, K-Means, and Deep Q-Networks, to enhance blockchain security. Experimental results show Deep Q-Networks and XGBoost outperform other models, achieving 97.8% accuracy and 0.99 AUC-ROC, demonstrating their effectiveness in real-time threat detection. This research highlights the potential of machine learning to safeguard blockchain systems and suggests future directions, such as federated learning for collaborative security and explainable AI for improved transparency.
Keywords
Blockchain security, machine learning, anomaly detection
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Copyright (c) 2024 Ashim Chandra Das, S M Shadul Islam Rishad, Pinky Akter, Sanjida Akter Tisha, Sadia Afrin, Farhan Shakil, Pritom Das, Mashaeikh Zaman Md. Eftakhar Choudhury, Md Mohibur Rahman
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