Adversarial Training for Proof-of-Work Consensus

Abstract

Proof-of-Work (PoW) consensus mechanisms, foundational to cryptocurrencies like Bitcoin, rely on computational difficulty to secure the network. However, this reliance introduces vulnerabilities. Traditional PoW systems are susceptible to targeted attacks where adversaries strategically generate blocks designed to disrupt consensus. This paper proposes a novel approach – adversarial training – to mitigate these vulnerabilities. We introduce a framework where a generator, mimicking an attacker, attempts to craft malicious blocks, while a discriminator learns to identify these blocks. Through iterative training, the system strengthens its defenses against such attacks, promoting robust consensus. This represents a significant departure from conventional PoW security strategies and offers a potentially more resilient approach to distributed ledger technology. We demonstrate the core principle through a conceptual model and outline a possible implementation strategy, highlighting the potential for future research and development.