A Concise Framework for AI-Driven Blockchain: Integrating DRL Consensus and GNN Security Auditing

Abstract

This research introduces a novel, high-performance hybrid framework merging Deep Reinforcement Learning (DRL) for dynamic consensus optimization with Graph Neural Networks (GNN) for advanced smart contract security auditing. Traditional blockchain architectures frequently struggle with balancing scalability and security under volatile transactional loads. By formulating consensus mechanism tuning as a DRL process and utilizing GNNs to model contract execution flows as relational graphs, our proposed approach achieves an impressive throughput of 3,450 TPS, reduces network latency down to 2.06 seconds, and delivers a robust 96.8% F1-score in preemptively detecting smart contract vulnerabilities. Keywords: Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), Blockchain Consensus, Smart Contract Auditing, Vulnerability Detection, Scalability Optimization.