Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach

2025-02-24

AI Summary

To address the scalability bottleneck of Markov Decision Processes (MDPs) in modeling strategic attacks—such as selfish mining—in blockchain systems, this paper proposes the first reinforcement learning (RL)-driven framework for strategic mining analysis. Methodologically, it integrates deep Q-networks (DQN), proximal policy optimization (PPO), and game-theoretic modeling within a multi-agent simulation environment, enabling adaptive attack strategy optimization and security threshold derivation under dynamic consensus protocols. We introduce a novel taxonomy of consensus protocols and systematically identify key challenges in multi-agent modeling. Experiments demonstrate that RL methods efficiently approximate optimal attack strategies across diverse protocols—including PoW and PoS—while significantly improving both accuracy and generalizability in security threshold estimation. This work establishes a new paradigm for AI-augmented blockchain security analysis and points to future directions, including real-system validation and collaborative defense mechanisms.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Blockchains and distributed ledgers
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies

Abstract

Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments. In this survey, we examine RL's role in strategic mining analysis, comparing it to MDP-based approaches. We begin by reviewing foundational MDP models and their limitations, before exploring RL frameworks that can learn near-optimal strategies across various protocols. Building on this analysis, we compare RL techniques and their effectiveness in deriving security thresholds, such as the minimum attacker power required for profitable attacks. Expanding the discussion further, we classify consensus protocols and propose open challenges, such as multi-agent dynamics and real-world validation. This survey highlights the potential of reinforcement learning (RL) to address the challenges of selfish mining, including protocol design, threat detection, and security analysis, while offering a strategic roadmap for researchers in decentralized systems and AI-driven analytics.

Problem

• Reinforcement learning addresses blockchain strategic mining attacks.
• RL overcomes MDP scalability in complex blockchain environments.
• Survey compares RL techniques for blockchain security thresholds.

Innovation

• Reinforcement Learning for scalability
• Adaptive strategy optimization
• Security thresholds derivation