Enhancing Blockchain Security: A Comprehensive Study on Fraud Detection and Prevention

Blockchain technology has revolutionized digital transactions by integrating transparent, secure, and decentralized financial systems. But it is endangered by critical vulnerabilities from defects such as Sybil attacks, selfish mining, and double-spending attacks. Researchers have explored various methods to minimize such threats, including hybrid consensus protocols, machine learning algorithms, Graph Neural Networks (GNNs), and mathematical modeling-based fraud prediction and prevention. To achieve maximum blockchain security, Meybodi introduces machine learning algorithms (SDTLA and WVBM), while Kang et al. propose a GNN-based model to precisely predict double-spending attacks. To address double-spending attacks, Akbar et al. propose a hybrid Proof-ofWork (PoW) and Proof-of-Stake (PoS), whereas Yuen et al. focus on Bitcoin Generator Scam (BGS) detection, addressing over-smoothing issues in GNNs.Other strategies for blockchain security improvement are Block Access Restriction (BAR) techniques, ECDSA-based prevention mechanisms, and GraphSAGE and Graph Attention Networks (GAT). A safer and stronger decentralized financial system is guaranteed by this paper's discussion of such methods, research gaps identification, and investigation of enhancements to enhance blockchain networks' capabilities in fraud detection, scalability, and flexibility.