Automated Selfish Mining Analysis for DAG-Based PoW Consensus Protocols

TL;DR

This paper introduces a generic, modular approach to analyze selfish mining strategies in DAG-based PoW protocols using automated MDP derivation and solving, streamlining protocol analysis and design.

Contribution

It presents a flexible attack model and tooling that automatically generates and solves MDPs for various DAG-based PoW protocols, reducing manual effort.

Findings

Automated MDP generation for multiple protocols

Broad applicability to DAG-based PoW systems

Facilitates faster protocol analysis and testing

Abstract

Selfish mining is strategic rule-breaking to maximize rewards in proof-of-work protocols. Markov Decision Processes (MDPs) are the preferred tool for finding optimal strategies in Bitcoin and similar linear chain protocols. Protocols increasingly adopt DAG-based chain structures, for which MDP analysis is more involved. To date, researchers have tailored specific MDPs for each protocol. Protocol design suffers long feedback loops, as each protocol change implies manual work on the MDP. To overcome this, we propose a generic attack model that covers a wide range of protocols, including Ethereum Proof-of-Work, GhostDAG, and Parallel Proof-of-Work. Our approach is modular: we specify each protocol as a concise program, and our tooling then derives and solves the selfish mining MDP automatically.