Incentive Attacks on DAG-Based Blockchains with Random Transaction Selection

blockchains, consensual protocols, incentive attacks, game theory, simulation, direct acyclic graph, proof-of-work

Several blockchain consensus protocols proposed to use Directed Acyclic Graphs (DAGs) to solve the limited processing throughput of traditional single-chain Proof-of-Work (PoW) blockchains. Many such protocols utilize a random transaction selection strategy (e.g., PHANTOM, GHOSTDAG, SPECTRE, Inclusive, and Prism) to avoid transaction duplicities across parallel blocks in DAG and thus maximize the network throughput. However, previous research has not rigorously examined incentive-oriented malicious behaviors when transaction selection deviates from the protocol, which motivated our research. In the scope of this work, we perform a game-theoretic analysis of the generic DAG-based blockchain protocol that uses the random transaction selection strategy, proving that such a strategy does not represent a Nash equilibrium. Furthermore, we develop a blockchain simulator that extends existing open-source tools to support multiple chains and explore incentive-based deviations from the protocol. Our simulations of simple network topology with ten miners confirm our conclusion from the game-theoretic analysis. The simulations show that malicious actors who do not follow the random transaction selection strategy can profit more than honest miners. This has a detrimental effect on the processing throughput of the protocol because duplicate transactions are included in more than one block of different chains. Moreover, we show that malicious miners are incentivized to form a shared mining pool to increase their profit. This undermines the network's decentralization and degrades the design of the protocols in question.