The Integration of Blockchain and Machine Learning for Secure Authentication and Tamper-Proof Transactions

Abstract

Digital ecosystems face escalating threats from sophisticated cyber-attacks and transaction fraud. To address these challenges, we propose a hybrid framework that seamlessly combines the decentralization and immutability of blockchain with real-time anomaly detection powered by machine learning. In our approach, a private Hyperledger Fabric network records all authentication and transaction events, while an Isolation Forest model flags abnormal behaviors before they are committed to the ledger. We evaluated the system on 973 blockchain transaction records, achieving a false-positive rate under 5% and successfully identifying 97 anomalies (≈9.97%). Average processing latency remained within acceptable bounds (≈2.25 seconds per event). This architecture ensures tamper-proof logging and proactive threat mitigation, making it suitable for deployment in finance, healthcare, and e-governance domains.