A Strong Adaptive, Strategic Double-Spending Attack on Blockchains

Abstract

In this paper, we first propose an adaptive strategy for double-spending attack on blockchains. The attacker in our strategy observes the length of the honest branch when a submitted transaction becomes available in the blockchain, and then updates the attack strategy accordingly. This provides a stronger strategy than conventional double-spending attack. We then derive closed-form expressions for the probability of a successful attack and the expected reward of attacker miners. Our analysis shows that the probability of a successful attack by convincing the network nodes to follow the counterfeit branch under the proposed attack strategy is 60% higher than what is expected from the conventional attack strategy when the attackers acquire 40% of the total network processing power. To counter this increase in the probability of attack, the network nodes are required to use a bigger number of confirmation blocks for validating any transaction in the blockchain. We computed the. expected reward of an attacker for mining a counterfeit branch on a blockchain and observed that the expected reward drops to zero after a few number of block confirmations.