Enhancing Blockchain Transaction Security: A Hybrid Machine Learning Approach for Fraud Detection
Keywords:
Blockchain, Smart Contract Vulnerabilities, Cyber Threat, Supervised Learning, Unsupervised Learning, Anomaly Detection, Transaction Integrity, Support Vector MachineAbstract
A newly proposed hybrid approach that makes use of both supervised and unsu pervised machine learning to implement security within blockchain transactions. Blockchain, despite its central role in the decentralized networks and the crypto graphic cryptography, is still open to high-end attacks. Making use of random forest, autoencoders, and SVM models to tap their strengths on classification and anomaly detection fights these threats. Normalization and feature selection tech niques boost the performance of a model. Thus, the hybrid model demonstrated above surpassing the performance of standalone models in fraud detection and mitigation indicates that this will be a future-proof solution fortified upon emerg ing threats behind secure digital finance in blockchain.
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