ENHANCING BLOCKCHAIN SECURITY USING MACHINE LEARNING-OPTIMIZED` ASYMMETRIC ENCRYPTION: A COMPREHENSIVE FRAMEWORK FOR INTELLIGENT CRYPTOGRAPHIC MANAGEMENT IN DISTRIBUTED LEDGER SYSTEMS

Abstract

Blockchain technology has emerged as a transformative paradigm for decentralized trust and transparent transaction processing across diverse sectors including finance, supply chain, healthcare, and digital identity management. However, the escalating sophistication of cyber threats, coupled with the computational rigidity of conventional cryptographic implementations, presents critical vulnerabilities in contemporary blockchain ecosystems. This paper proposes a novel Machine Learning-Optimized Asymmetric Encryption Framework (ML-OAEF) that integrates advanced supervised learning algorithms with intelligent cryptographic management to enhance blockchain security, scalability, and adaptive resilience. We present a comprehensive methodology encompassing dataset synthesis, multi-dimensional feature engineering, comparative model evaluation, and blockchain-specific security assessment. Four distinct machine learning architectures—Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP)—were systematically evaluated against a diverse cryptographic dataset comprising 1,647 samples across symmetric encryption (AES, DES, 3DES, Blowfish, RC4, ChaCha20), asymmetric encryption (RSA), and hash functions (SHA-256). Experimental results demonstrate that the Support Vector Machine and Neural Network models achieved exceptional classification accuracy of 96.5%, significantly outperforming traditional baseline approaches. We introduce a Composite Blockchain Security Score (CBSS) metric that quantifies cryptographic suitability across five dimensions: cryptographic strength, performance efficiency, quantum resistance, blockchain compatibility, and machine learning confidence. Furthermore, we propose a Blockchain Integration Mechanism (BIM) that operationalizes ML-driven insights across the data, consensus, and application layers of blockchain architecture. The developed real-time ML pipeline achieved 83.75% verification accuracy in live blockchain monitoring scenarios, confirming practical applicability for automated cryptographic verification and anomaly detection. This research establishes a foundation for next-generation blockchain security systems that leverage artificial intelligence to dynamically optimize cryptographic configurations, detect emerging threats, and ensure post-quantum readiness. The proposed framework bridges the gap between data-driven intelligence and decentralized trust mechanisms, offering a scalable, interpretable, and future-ready solution for securing distributed ledger technologies.

Keywords : Blockchain Security, Machine Learning, Asymmetric Encryption, Cryptographic Algorithm Identification, Deep Learning, Post-Quantum Cryptography, Zero-Knowledge Proofs, Federated Learning, Smart Contracts, Distributed Ledger Technology