Ergodic Mean-Payoff Games for the Analysis of Attacks in Crypto-Currencies
This addresses security analysis for crypto-currency protocols by enabling stateful and quantitative modeling, though it is incremental as it builds on existing game-theoretic approaches.
The authors tackled the problem of analyzing attacks in crypto-currencies by developing a framework using ergodic mean-payoff games to model incentives and long-term gains, achieving scalability to handle games with thousands of states and millions of transitions.
Crypto-currencies are digital assets designed to work as a medium of exchange, e.g., Bitcoin, but they are susceptible to attacks (dishonest behavior of participants). A framework for the analysis of attacks in crypto-currencies requires (a) modeling of game-theoretic aspects to analyze incentives for deviation from honest behavior; (b) concurrent interactions between participants; and (c) analysis of long-term monetary gains. Traditional game-theoretic approaches for the analysis of security protocols consider either qualitative temporal properties such as safety and termination, or the very special class of one-shot (stateless) games. However, to analyze general attacks on protocols for crypto-currencies, both stateful analysis and quantitative objectives are necessary. In this work our main contributions are as follows: (a) we show how a class of concurrent mean-payoff games, namely ergodic games, can model various attacks that arise naturally in crypto-currencies; (b) we present the first practical implementation of algorithms for ergodic games that scales to model realistic problems for crypto-currencies; and (c) we present experimental results showing that our framework can handle games with thousands of states and millions of transitions.