Enhancing Blockchain Security Through Hybrid PoS–PBFT Consensus and Machine Learning-Based Anomaly Detection

Abstract

Blockchain technology has emerged as a secure and decentralized solution for data management across various domains. However, existing consensus mechanisms face challenges related to security, scalability, and energy efficiency, while blockchains remain vulnerable to sophisticated attacks such as double spending, selfish mining, and Sybil attacks. This paper proposes a novel hybrid blockchain security framework that integrates a Hybrid Consensus Algorithm (HCA) combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) with Machine Learning based attack detection. The hybrid consensus improves transaction finality and reduces energy consumption, while the ML module detects anomalous behaviors in real time. Experimental evaluation using a private Ethereum based blockchain demonstrates that the proposed approach improves attack detection accuracy up to 96.8 %, reduces consensus latency by 34 %, and enhances throughput by 27 % compared to traditional PoW based systems. The results confirm that integrating hybrid consensus with intelligent security mechanisms significantly strengthens blockchain resilience.